Subtitles appear on screen as short pieces of text, segmented based on formal constraints (length) and syntactic/semantic criteria. Subtitle segmentation can be evaluated with sequence segmentation metrics against a human reference. However, standard segmentation metrics cannot be applied when systems generate outputs different than the reference, e.g. with end-to-end subtitling systems. In this paper, we study ways to conduct reference-based evaluations of segmentation accuracy irrespective of the textual content. We first conduct a systematic analysis of existing metrics for evaluating subtitle segmentation. We then introduce Sigma, a Subtitle Segmentation Score derived from an approximate upper-bound of BLEU on segmentation boundaries, which allows us to disentangle the effect of good segmentation from text quality. To compare Sigma with existing metrics, we further propose a boundary projection method from imperfect hypotheses to the true reference. Results show that all metrics are able to reward high quality output but for similar outputs system ranking depends on each metric’s sensitivity to error type. Our thorough analyses suggest Sigma is a promising segmentation candidate but its reliability over other segmentation metrics remains to be validated through correlations with human judgements.
La segmentation automatique en mots et en morphèmes est une étape cruciale dans le processus de documentation des langues. Dans ce travail, nous étudions plusieurs modèles bayésiens pour réaliser une segmentation conjointe des phrases à ces deux niveaux : d’une part, en introduisant un couplage déterministe entre deux modèles spécialisés pour identifier chaque type de frontières, d’autre part, en proposant une modélisation intrinsèquement hiérarchique. Un objectif important de cette étude est de comparer ces modèles dans un scénario où une supervision faible est disponible. Nos expériences portent sur deux langues et permettent de comparer dans des conditions réalistes les mérites de ces diverses modélisations.
Cet article s’intéresse au transfert cross-lingue d’analyseurs en dépendances et étudie des méthodes pour limiter l’effet potentiellement néfaste pour le transfert de divergences entre l’ordre des mots dans les langues source et cible. Nous montrons comment apprendre et implémenter des stratégies de réordonnancement, qui, utilisées en prétraitement, permettent souvent d’améliorer les performances des analyseurs dans un scénario de transfert « zero-shot ».
Ce travail présente deux séries d’expériences visant à identifier les flux d’information dans les systèmes de traduction neuronaux. La première série s’appuie sur une comparaison des décisions d’un modèle de langue et d’un modèle de traduction pour mettre en évidence le flux d’information provenant de la source. La seconde série met en évidence l’impact de ces flux sur l’apprentissage du système dans le cas particulier du transfert de l’information de genre.
Word and morpheme segmentation are fundamental steps of language documentation as they allow to discover lexical units in a language for which the lexicon is unknown. However, in most language documentation scenarios, linguists do not start from a blank page: they may already have a pre-existing dictionary or have initiated manual segmentation of a small part of their data. This paper studies how such a weak supervision can be taken advantage of in Bayesian non-parametric models of segmentation. Our experiments on two very low resource languages (Mboshi and Japhug), whose documentation is still in progress, show that weak supervision can be beneficial to the segmentation quality. In addition, we investigate an incremental learning scenario where manual segmentations are provided in a sequential manner. This work opens the way for interactive annotation tools for documentary linguists.
Multiple studies have shown that existing NMT systems demonstrate some kind of “gender bias”. As a result, MT output appears to err more often for feminine forms and to amplify social gender misrepresentations, which is potentially harmful to users and practioners of these technologies. This paper continues this line of investigations and reports results obtained with a new test set in strictly controlled conditions. This setting allows us to better understand the multiple inner mechanisms that are causing these biases, which include the linguistic expressions of gender, the unbalanced distribution of masculine and feminine forms in the language, the modelling of morphological variation and the training process dynamics. To counterbalance these effects, we formulate several proposals and notably show that modifying the training loss can effectively mitigate such biases.
After a period of decrease, interest in word alignments is increasing again for their usefulness in domains such as typological research, cross-lingual annotation projection and machine translation. Generally, alignment algorithms only use bitext and do not make use of the fact that many parallel corpora are multiparallel. Here, we compute high-quality word alignments between multiple language pairs by considering all language pairs together. First, we create a multiparallel word alignment graph, joining all bilingual word alignment pairs in one graph. Next, we use graph neural networks (GNNs) to exploit the graph structure. Our GNN approach (i) utilizes information about the meaning, position and language of the input words, (ii) incorporates information from multiple parallel sentences, (iii) adds and removes edges from the initial alignments, and (iv) yields a prediction model that can generalize beyond the training sentences. We show that community detection algorithms can provide valuable information for multiparallel word alignment. Our method outperforms previous work on three word alignment datasets and on a downstream task.
Multidomain and multilingual machine translation often rely on parameter sharing strategies, where large portions of the network are meant to capture the commonalities of the tasks at hand, while smaller parts are reserved to model the peculiarities of a language or a domain. In adapter-based approaches, these strategies are hardcoded in the network architecture, independent of the similarities between tasks. In this work, we propose a new method to better take advantage of these similarities, using a latent-variable model. We also develop new techniques to train this model end-to-end and report experimental results showing that the learned patterns are both meaningful and yield improved translation performance without any increase of the model size.
Part-of-Speech (POS) tagging is an important component of the NLP pipeline, but many low-resource languages lack labeled data for training. An established method for training a POS tagger in such a scenario is to create a labeled training set by transferring from high-resource languages. In this paper, we propose a novel method for transferring labels from multiple high-resource source to low-resource target languages. We formalize POS tag projection as graph-based label propagation. Given translations of a sentence in multiple languages, we create a graph with words as nodes and alignment links as edges by aligning words for all language pairs. We then propagate node labels from source to target using a Graph Neural Network augmented with transformer layers. We show that our propagation creates training sets that allow us to train POS taggers for a diverse set of languages. When combined with enhanced contextualized embeddings, our method achieves a new state-of-the-art for unsupervised POS tagging of low-resource languages.
Machine Translation (MT) is usually viewed as a one-shot process that generates the target language equivalent of some source text from scratch. We consider here a more general setting which assumes an initial target sequence, that must be transformed into a valid translation of the source, thereby restoring parallelism between source and target. For this bilingual synchronization task, we consider several architectures (both autoregressive and non-autoregressive) and training regimes, and experiment with multiple practical settings such as simulated interactive MT, translating with Translation Memory (TM) and TM cleaning. Our results suggest that one single generic edit-based system, once fine-tuned, can compare with, or even outperform, dedicated systems specifically trained for these tasks.
As the amount of audio-visual content increases, the need to develop automatic captioning and subtitling solutions to match the expectations of a growing international audience appears as the only viable way to boost throughput and lower the related post-production costs. Automatic captioning and subtitling often need to be tightly intertwined to achieve an appropriate level of consistency and synchronization with each other and with the video signal. In this work, we assess a dual decoding scheme to achieve a strong coupling between these two tasks and show how adequacy and consistency are increased, with virtually no additional cost in terms of model size and training complexity.
Building effective Neural Machine Translation models often implies accommodating diverse sets of heterogeneous data so as to optimize performance for the domain(s) of interest. Such multi-source / multi-domain adaptation problems are typically approached through instance selection or reweighting strategies, based on a static assessment of the relevance of training instances with respect to the task at hand. In this paper, we study dynamic data selection strategies that are able to automatically re-evaluate the usefulness of data samples and to evolve a data selection policy in the course of training. Based on the results of multiple experiments, we show that such methods constitute a generic framework to automatically and effectively handle a variety of real-world situations, from multi-source domain adaptation to multi-domain learning and unsupervised domain adaptation.
With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research more recently. Alignments are useful for typological research, transferring formatting like markup to translated texts, and can be used in the decoding of machine translation systems. At the same time, massively multilingual processing is becoming an important NLP scenario, and pretrained language and machine translation models that are truly multilingual are proposed. However, most alignment algorithms rely on bitexts only and do not leverage the fact that many parallel corpora are multiparallel. In this work, we exploit the multiparallelity of corpora by representing an initial set of bilingual alignments as a graph and then predicting additional edges in the graph. We present two graph algorithms for edge prediction: one inspired by recommender systems and one based on network link prediction. Our experimental results show absolute improvements in F1 of up to 28% over the baseline bilingual word aligner in different datasets.
Machine translation is generally understood as generating one target text from an input source document. In this paper, we consider a stronger requirement: to jointly generate two texts so that each output side effectively depends on the other. As we discuss, such a device serves several practical purposes, from multi-target machine translation to the generation of controlled variations of the target text. We present an analysis of possible implementations of dual decoding, and experiment with four applications. Viewing the problem from multiple angles allows us to better highlight the challenges of dual decoding and to also thoroughly analyze the benefits of generating matched, rather than independent, translations.
When building machine translation systems, one often needs to make the best out of heterogeneous sets of parallel data in training, and to robustly handle inputs from unexpected domains in testing. This multi-domain scenario has attracted a lot of recent work that fall under the general umbrella of transfer learning. In this study, we revisit multi-domain machine translation, with the aim to formulate the motivations for developing such systems and the associated expectations with respect to performance. Our experiments with a large sample of multi-domain systems show that most of these expectations are hardly met and suggest that further work is needed to better analyze the current behaviour of multi-domain systems and to make them fully hold their promises.
This paper aims at identifying the information flow in state-of-the-art machine translation systems, taking as example the transfer of gender when translating from French into English. Using a controlled set of examples, we experiment several ways to investigate how gender information circulates in a encoder-decoder architecture considering both probing techniques as well as interventions on the internal representations used in the MT system. Our results show that gender information can be found in all token representations built by the encoder and the decoder and lead us to conclude that there are multiple pathways for gender transfer.
Word alignment identify translational correspondences between words in a parallel sentence pair and are used and for example and to train statistical machine translation and learn bilingual dictionaries or to perform quality estimation. Subword tokenization has become a standard preprocessing step for a large number of applications and notably for state-of-the-art open vocabulary machine translation systems. In this paper and we thoroughly study how this preprocessing step interacts with the word alignment task and propose several tokenization strategies to obtain well-segmented parallel corpora. Using these new techniques and we were able to improve baseline word-based alignment models for six language pairs.
This paper describes LISN’s submissions to two shared tasks at WMT’21. For the biomedical translation task, we have developed resource-heavy systems for the English-French language pair, using both out-of-domain and in-domain corpora. The target genre for this task (scientific abstracts) corresponds to texts that often have a standardized structure. Our systems attempt to take this structure into account using a hierarchical system of sentence-level tags. Translation systems were also prepared for the News task for the French-German language pair. The challenge was to perform unsupervised adaptation to the target domain (financial news). For this, we explored the potential of retrieval-based strategies, where sentences that are similar to test instances are used to prime the decoder.
Code-Switching (CSW) is a common phenomenon that occurs in multilingual geographic or social contexts, which raises challenging problems for natural language processing tools. We focus here on Machine Translation (MT) of CSW texts, where we aim to simultaneously disentangle and translate the two mixed languages. Due to the lack of actual translated CSW data, we generate artificial training data from regular parallel texts. Experiments show this training strategy yields MT systems that surpass multilingual systems for code-switched texts. These results are confirmed in an alternative task aimed at providing contextual translations for a L2 writing assistant.
Cet article présente les premiers résultats d’une étude en cours sur les biais de genre dans les corpus d’entraînements et dans les systèmes de traduction neuronale. Nous étudions en particulier un corpus minimal et contrôlé pour mesurer l’intensité de ces biais dans les deux directions anglais-français et français-anglais ; ce cadre contrôlé nous permet également d’analyser les représentations internes manipulées par le système pour réaliser ses prédictions lexicales, ainsi que de formuler des hypothèses sur la manière dont ce biais se distribue dans les représentations du système.
Une façon de réaliser un sous-titrage automatique monolingue est d’associer un système de reconnaissance de parole avec un modèle de traduction de la transcription vers les sous-titres. La tâche de « traduction » est délicate dans la mesure où elle doit opérer une simplification et une compression du texte, respecter des normes liées à l’affichage, tout en composant avec les erreurs issues de la reconnaissance vocale. Une difficulté supplémentaire est la relative rareté des corpus mettant en parallèle transcription automatique et sous-titres sont relativement rares. Nous décrivons ici un nouveau corpus en cours de constitution et nous expérimentons l’utilisation de méthodes de contrôle plus ou moins direct de la longueur des phrases engendrées, afin d’améliorer leur qualité du point de vue linguistique et normatif.
Word alignments are useful for tasks like statistical and neural machine translation (NMT) and cross-lingual annotation projection. Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT. However, most approaches require parallel training data and quality decreases as less training data is available. We propose word alignment methods that require no parallel data. The key idea is to leverage multilingual word embeddings – both static and contextualized – for word alignment. Our multilingual embeddings are created from monolingual data only without relying on any parallel data or dictionaries. We find that alignments created from embeddings are superior for four and comparable for two language pairs compared to those produced by traditional statistical aligners – even with abundant parallel data; e.g., contextualized embeddings achieve a word alignment F1 for English-German that is 5 percentage points higher than eflomal, a high-quality statistical aligner, trained on 100k parallel sentences.
Priming is a well known and studied psychology phenomenon based on the prior presentation of one stimulus (cue) to influence the processing of a response. In this paper, we propose a framework to mimic the process of priming in the context of neural machine translation (NMT). We evaluate the effect of using similar translations as priming cues on the NMT network. We propose a method to inject priming cues into the NMT network and compare our framework to other mechanisms that perform micro-adaptation during inference. Overall, experiments conducted in a multi-domain setting confirm that adding priming cues in the NMT decoder can go a long way towards improving the translation accuracy. Besides, we show the suitability of our framework to gather valuable information for an NMT network from monolingual resources.
Domain adaptation is an old and vexing problem for machine translation systems. The most common approach and successful to supervised adaptation is to fine-tune a baseline system with in-domain parallel data. Standard fine-tuning however modifies all the network parameters, which makes this approach computationally costly and prone to overfitting. A recent, lightweight approach, instead augments a baseline model with supplementary (small) adapter layers, keeping the rest of the mode unchanged. This has the additional merit to leave the baseline model intact, and adaptable to multiple domains. In this paper, we conduct a thorough analysis of the adapter model in the context of a multidomain machine translation task. We contrast multiple implementations of this idea on two language pairs. Our main conclusions are that residual adapters provide a fast and cheap method for supervised multi-domain adaptation; our two variants prove as effective as the original adapter model, and open perspective to also make adapted models more robust to label domain errors.
This paper describes LIMSI’s submissions to the translation shared tasks at WMT’20. This year we have focused our efforts on the biomedical translation task, developing a resource-heavy system for the translation of medical abstracts from English into French, using back-translated texts, terminological resources as well as multiple pre-processing pipelines, including pre-trained representations. Systems were also prepared for the robustness task for translating from English into German; for this large-scale task we developed multi-domain, noise-robust, translation systems aim to handle the two test conditions: zero-shot and few-shot domain adaptation.
La simplification de textes a émergé comme un sous-domaine actif du traitement automatique des langues, du fait des problèmes pratiques et théoriques qu’elle permet d’aborder, ainsi que de ses nombreuses applications pratiques. Des corpus de simplification sont nécessaires pour entrainer des systèmes de simplification automatique ; ces ressources sont toutefois rares et n’existent que pour un petit nombre de langues. Nous montrons ici que dans un contexte où les ressources pour la simplification sont rares, il reste néanmoins possible de construire des systèmes de simplification, en ayant recours à des corpus synthétiques, par exemple obtenus par traduction automatique, et nous évaluons diverses manières de les constituer.
Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI – including many opportunities, synergies but also misconceptions – has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.
The performance of Part-of-Speech tagging varies significantly across the treebanks of the Universal Dependencies project. This work points out that these variations may result from divergences between the annotation of train and test sets. We show how the annotation variation principle, introduced by Dickinson and Meurers (2003) to automatically detect errors in gold standard, can be used to identify inconsistencies between annotations; we also evaluate their impact on prediction performance.
This article studies the relationship between text readability indice and automatic machine understanding systems. Our hypothesis is that the simpler a text is, the better it should be understood by a machine. We thus expect to a strong correlation between readability levels on the one hand, and performance of automatic reading systems on the other hand. We test this hypothesis with several understanding systems based on language models of varying strengths, measuring this correlation on two corpora of journalistic texts. Our results suggest that this correlation is rather small that existing comprehension systems are far to reproduce the gradual improvement of their performance on texts of decreasing complexity.
Word alignments identify translational correspondences between words in a parallel sentence pair and is used, for instance, to learn bilingual dictionaries, to train statistical machine translation systems, or to perform quality estimation. In most areas of natural lan- guage processing, neural network models nowadays constitute the preferred approach, a situation that might also apply to word align- ment models. In this work, we study and comprehensively evaluate neural models for unsupervised word alignment for four language pairs, contrasting several variants of neural models. We show that in most settings, neural versions of the IBM-1 and hidden Markov models vastly outperform their discrete counterparts. We also analyze typical alignment errors of the baselines that our models over- come to illustrate the benefits — and the limitations — of these new models for morphologically rich languages.
Supervised machine translation works well when the train and test data are sampled from the same distribution. When this is not the case, adaptation techniques help ensure that the knowledge learned from out-of-domain texts generalises to in-domain sentences. We study here a related setting, multi-domain adaptation, where the number of domains is potentially large and adapting separately to each domain would waste training resources. Our proposal transposes to neural machine translation the feature expansion technique of (Daumé III, 2007): it isolates domain-agnostic from domain-specific lexical representations, while sharing the most of the network across domains. Our experiments use two architectures and two language pairs: they show that our approach, while simple and computationally inexpensive, outperforms several strong baselines and delivers a multi-domain system that successfully translates texts from diverse sources.
One of the basic tasks of computational language documentation (CLD) is to identify word boundaries in an unsegmented phonemic stream. While several unsupervised monolingual word segmentation algorithms exist in the literature, they are challenged in real-world CLD settings by the small amount of available data. A possible remedy is to take advantage of glosses or translation in a foreign, well- resourced, language, which often exist for such data. In this paper, we explore and compare ways to exploit neural machine translation models to perform unsupervised boundary detection with bilingual information, notably introducing a new loss function for jointly learning alignment and segmentation. We experiment with an actual under-resourced language, Mboshi, and show that these techniques can effectively control the output segmentation length.
Not all dependencies are equal when training a dependency parser: some are straightforward enough to be learned with only a sample of data, others embed more complexity. This work introduces a series of metrics to quantify those differences, and thereby to expose the shortcomings of various parsing algorithms and strategies. Apart from a more thorough comparison of parsing systems, these new tools also prove useful for characterizing the information conveyed by cross-lingual parsers, in a quantitative but still interpretable way.
Corpus-based approaches to machine translation rely on the availability of clean parallel corpora. Such resources are scarce, and because of the automatic processes involved in their preparation, they are often noisy. This paper describes an unsupervised method for detecting translation divergences in parallel sentences. We rely on a neural network that computes cross-lingual sentence similarity scores, which are then used to effectively filter out divergent translations. Furthermore, similarity scores predicted by the network are used to identify and fix some partial divergences, yielding additional parallel segments. We evaluate these methods for English-French and English-German machine translation tasks, and show that using filtered/corrected corpora actually improves MT performance.
This work introduces a new strategy to compare the numerous conventions that have been proposed over the years for expressing dependency structures and discover the one for which a parser will achieve the highest parsing performance. Instead of associating each sentence in the training set with a single gold reference we propose to consider a set of references encoding alternative syntactic representations. Training a parser with a dynamic oracle will then automatically select among all alternatives the reference that will be predicted with the highest accuracy. Experiments on the UD corpora show the validity of this approach.
Because the most common transition systems are projective, training a transition-based dependency parser often implies to either ignore or rewrite the non-projective training examples, which has an adverse impact on accuracy. In this work, we propose a simple modification of dynamic oracles, which enables the use of non-projective data when training projective parsers. Evaluation on 73 treebanks shows that our method achieves significant gains (+2 to +7 UAS for the most non-projective languages) and consistently outperforms traditional projectivization and pseudo-projectivization approaches.
Le nouvel état de l’art en traduction automatique (TA) s’appuie sur des méthodes neuronales, qui différent profondément des méthodes utilisées antérieurement. Les métriques automatiques classiques sont mal adaptées pour rendre compte de la nature du saut qualitatif observé. Cet article propose un protocole d’évaluation pour la traduction de l’anglais vers le français spécifiquement focalisé sur la compétence morphologique des systèmes de TA, en étudiant leurs performances sur différents phénomènes grammaticaux.
Ce travail montre que la dégradation des performances souvent observée lors de l’application d’un analyseur morpho-syntaxique à des données hors domaine résulte souvent d’incohérences entre les annotations des ensembles de test et d’apprentissage. Nous montrons comment le principe de variation des annotations, introduit par Dickinson & Meurers (2003) pour identifier automatiquement les erreurs d’annotation, peut être utilisé pour identifier ces incohérences et évaluer leur impact sur les performances des analyseurs morpho-syntaxiques.
Computational Language Documentation attempts to make the most recent research in speech and language technologies available to linguists working on language preservation and documentation. In this paper, we pursue two main goals along these lines. The first is to improve upon a strong baseline for the unsupervised word discovery task on two very low-resource Bantu languages, taking advantage of the expertise of linguists on these particular languages. The second consists in exploring the Adaptor Grammar framework as a decision and prediction tool for linguists studying a new language. We experiment 162 grammar configurations for each language and show that using Adaptor Grammars for word segmentation enables us to test hypotheses about a language. Specializing a generic grammar with language specific knowledge leads to great improvements for the word discovery task, ultimately achieving a leap of about 30% token F-score from the results of a strong baseline.
Neural Machine Translation (MT) has radically changed the way systems are developed. A major difference with the previous generation (Phrase-Based MT) is the way monolingual target data, which often abounds, is used in these two paradigms. While Phrase-Based MT can seamlessly integrate very large language models trained on billions of sentences, the best option for Neural MT developers seems to be the generation of artificial parallel data through back-translation - a technique that fails to fully take advantage of existing datasets. In this paper, we conduct a systematic study of back-translation, comparing alternative uses of monolingual data, as well as multiple data generation procedures. Our findings confirm that back-translation is very effective and give new explanations as to why this is the case. We also introduce new data simulation techniques that are almost as effective, yet much cheaper to implement.
Progress in the quality of machine translation output calls for new automatic evaluation procedures and metrics. In this paper, we extend the Morpheval protocol introduced by Burlot and Yvon (2017) for the English-to-Czech and English-to-Latvian translation directions to three additional language pairs, and report its use to analyze the results of WMT 2018’s participants for these language pairs. Considering additional, typologically varied source and target languages also enables us to draw some generalizations regarding this morphology-oriented evaluation procedure.
Lorsqu’ils sont traduits depuis une langue à morphologie riche vers l’anglais, les mots-formes sources contiennent des marques d’informations grammaticales pouvant être jugées redondantes par rapport à l’anglais, causant une variabilité formelle qui nuit à l’estimation des modèles probabilistes. Un moyen bien documenté pour atténuer ce problème consiste à supprimer l’information non pertinente de la source en la normalisant. Ce pré-traitement est généralement effectué de manière déterministe, à l’aide de règles produites manuellement. Une telle normalisation est, par essence, sous-optimale et doit être adaptée pour chaque paire de langues. Nous présentons, dans cet article, une méthode simple pour rechercher automatiquement une normalisation optimale de la morphologie source par rapport à la langue cible et montrons que celle-ci peut améliorer la traduction automatique.
Ce travail cherche à comprendre pourquoi les performances d’un analyseur morpho-syntaxiques chutent fortement lorsque celui-ci est utilisé sur des données hors domaine. Nous montrons à l’aide d’une expérience jouet que ce comportement peut être dû à un phénomène de masquage des caractéristiques lexicalisées par les caractéristiques non lexicalisées. Nous proposons plusieurs modèles essayant de réduire cet effet.
The computational complexity of linear-chain Conditional Random Fields (CRFs) makes it difficult to deal with very large label sets and long range dependencies. Such situations are not rare and arise when dealing with morphologically rich languages or joint labelling tasks. We extend here recent proposals to consider variable order CRFs. Using an effective finite-state representation of variable-length dependencies, we propose new ways to perform feature selection at large scale and report experimental results where we outperform strong baselines on a tagging task.
This paper describes LIMSI’s submission to the CoNLL 2017 UD Shared Task, which is focused on small treebanks, and how to improve low-resourced parsing only by ad hoc combination of multiple views and resources. We present our approach for low-resourced parsing, together with a detailed analysis of the results for each test treebank. We also report extensive analysis experiments on model selection for the PUD treebanks, and on annotation consistency among UD treebanks.
This paper formalizes a sound extension of dynamic oracles to global training, in the frame of transition-based dependency parsers. By dispensing with the pre-computation of references, this extension widens the training strategies that can be entertained for such parsers; we show this by revisiting two standard training procedures, early-update and max-violation, to correct some of their search space sampling biases. Experimentally, on the SPMRL treebanks, this improvement increases the similarity between the train and test distributions and yields performance improvements up to 0.7 UAS, without any computation overhead.
Resources for evaluating sentence-level and word-level alignment algorithms are unsatisfactory. Regarding sentence alignments, the existing data is too scarce, especially when it comes to difficult bitexts, containing instances of non-literal translations. Regarding word-level alignments, most available hand-aligned data provide a complete annotation at the level of words that is difficult to exploit, for lack of a clear semantics for alignment links. In this study, we propose new methodologies for collecting human judgements on alignment links, which have been used to annotate 4 new data sets, at the sentence and at the word level. These will be released online, with the hope that they will prove useful to evaluate alignment software and quality estimation tools for automatic alignment. Keywords: Parallel corpora, Sentence Alignments, Word Alignments, Confidence Estimation
Because of the small size of Romanian corpora, the performance of a PoS tagger or a dependency parser trained with the standard supervised methods fall far short from the performance achieved in most languages. That is why, we apply state-of-the-art methods for cross-lingual transfer on Romanian tagging and parsing, from English and several Romance languages. We compare the performance with monolingual systems trained with sets of different sizes and establish that training on a few sentences in target language yields better results than transferring from large datasets in other languages.
Cet article présente une méthode simple de transfert cross-lingue de dépendances. Nous montrons tout d’abord qu’il est possible d’apprendre un analyseur en dépendances par transition à partir de données partiellement annotées. Nous proposons ensuite de construire de grands ensembles de données partiellement annotés pour plusieurs langues cibles en projetant les dépendances via les liens d’alignement les plus sûrs. En apprenant des analyseurs pour les langues cibles à partir de ces données partielles, nous montrons que cette méthode simple obtient des performances qui rivalisent avec celles de méthodes état-de-l’art récentes, tout en ayant un coût algorithmique moindre.
Dans cet article, nous proposons trois améliorations simples pour l’apprentissage global d’analyseurs en dépendances par transition de type A RC E AGER : un oracle non déterministe, la reprise sur le même exemple après une mise à jour et l’entraînement en configurations sous-optimales. Leur combinaison apporte un gain moyen de 0,2 UAS sur le corpus SPMRL. Nous introduisons également un cadre général permettant la comparaison systématique de ces stratégies et de la plupart des variantes connues. Nous montrons que la littérature n’a étudié que quelques stratégies parmi les nombreuses variations possibles, négligeant ainsi plusieurs pistes d’améliorations potentielles.
Le travail qui a conduit à cette démonstration combine des outils de traitement des langues multilingues, en particulier l’alignement automatique, avec des techniques de visualisation et d’interaction. Il vise à proposer des pistes pour le développement d’outils permettant de lire simultanément les différentes versions d’un texte disponible en plusieurs langues, avec des applications en lecture de loisir ou en lecture professionnelle.
This paper describes a two-step machine translation system that addresses the issue of translating into a morphologically rich language (English to Czech), by performing separately the translation and the generation of target morphology. The first step consists in translating from English into a normalized version of Czech, where some morphological information has been removed. The second step retrieves this information and re-inflects the normalized output, turning it into fully inflected Czech. We introduce different setups for the second step and evaluate the quality of their predictions over different MT systems trained on different amounts of parallel and monolingual data and report ways to adapt to different data sizes, which improves the translation in low-resource conditions, as well as when large training data is available.
This paper describes LIMSI’s submission to the MT track of IWSLT 2016. We report results for translation from English into Czech. Our submission is an attempt to address the difficulties of translating into a morphologically rich language by paying special attention to the morphology generation on target side. To this end, we propose two ways of improving the morphological fluency of the output: 1. by performing translation and inflection of the target language in two separate steps, and 2. by using a neural language model with characted-based word representation. We finally present the combination of both methods used for our primary system submission.
This paper studies cross-lingual transfer for dependency parsing, focusing on very low-resource settings where delexicalized transfer is the only fully automatic option. We show how to boost parsing performance by rewriting the source sentences so as to better match the linguistic regularities of the target language. We contrast a data-driven approach with an approach relying on linguistically motivated rules automatically extracted from the World Atlas of Language Structures. Our findings are backed up by experiments involving 40 languages. They show that both approaches greatly outperform the baseline, the knowledge-driven method yielding the best accuracies, with average improvements of +2.9 UAS, and up to +90 UAS (absolute) on some frequent PoS configurations.
Sentence compression is a way to perform text simplification and is usually handled in a monolingual setting. In this paper, we study ways to extend sentence compression in a bilingual context, where the goal is to obtain parallel compressions of parallel sentences. This can be beneficial for a series of multilingual natural language processing (NLP) tasks. We compare two ways to take bilingual information into account when compressing parallel sentences. Their efficiency is contrasted on a parallel corpus of News articles.
De nombreuses méthodes ont été proposées pour accélérer la prédiction d’objets structurés (tels que les arbres ou les séquences), ou pour permettre la prise en compte de dépendances plus riches afin d’améliorer les performances de la prédiction. Ces méthodes reposent généralement sur des techniques d’inférence approchée et ne bénéficient d’aucune garantie théorique aussi bien du point de vue de la qualité de la solution trouvée que du point de vue de leur critère d’apprentissage. Dans ce travail, nous étudions une nouvelle formulation de l’apprentissage structuré qui consiste à voir celui-ci comme un processus incrémental au cours duquel la sortie est construite de façon progressive. Ce cadre permet de formaliser plusieurs approches de prédiction structurée existantes. Grâce au lien que nous faisons entre apprentissage structuré et apprentissage par renforcement, nous sommes en mesure de proposer une méthode théoriquement bien justifiée pour apprendre des méthodes d’inférence approchée. Les expériences que nous réalisons sur quatre tâches de TAL valident l’approche proposée.
Quand on dispose de connaissances a priori sur les sorties possibles d’un problème d’étiquetage, il semble souhaitable d’inclure cette information lors de l’apprentissage pour simplifier la tâche de modélisation et accélérer les traitements. Pourtant, même lorsque ces contraintes sont correctes et utiles au décodage, leur utilisation lors de l’apprentissage peut dégrader sévèrement les performances. Dans cet article, nous étudions ce paradoxe et montrons que le manque de contraste induit par les connaissances entraîne une forme de sous-apprentissage qu’il est cependant possible de limiter.
Alors que les réseaux neuronaux occupent une place de plus en plus importante dans le traitement automatique des langues, les méthodes d’apprentissage actuelles utilisent pour la plupart des critères qui sont décorrélés de l’application. Cet article propose un nouveau cadre d’apprentissage discriminant pour l’estimation des modèles continus de traduction. Ce cadre s’appuie sur la définition d’un critère d’optimisation permettant de prendre en compte d’une part la métrique utilisée pour l’évaluation de la traduction et d’autre part l’intégration de ces modèles au sein des systèmes de traduction automatique. De plus, cette méthode d’apprentissage est comparée aux critères existants d’estimation que sont le maximum de vraisemblance et l’estimation contrastive bruitée. Les expériences menées sur la tâches de traduction des séminaires TED Talks de l’anglais vers le français montrent la pertinence d’un cadre discriminant d’apprentissage, dont les performances restent toutefois très dépendantes du choix d’une stratégie d’initialisation idoine. Nous montrons qu’avec une initialisation judicieuse des gains significatifs en termes de scores BLEU peuvent être obtenus.
Literary works are becoming increasingly available in electronic formats, thus quickly transforming editorial processes and reading habits. In the context of the global enthusiasm for multilingualism, the rapid spread of e-book readers, such as Amazon Kindle R or Kobo Touch R , fosters the development of a new generation of reading tools for bilingual books. In particular, literary works, when available in several languages, offer an attractive perspective for self-development or everyday leisure reading, but also for activities such as language learning, translation or literary studies. An important issue in the automatic processing of multilingual e-books is the alignment between textual units. Alignment could help identify corresponding text units in different languages, which would be particularly beneficial to bilingual readers and translation professionals. Computing automatic alignments for literary works, however, is a task more challenging than in the case of better behaved corpora such as parliamentary proceedings or technical manuals. In this paper, we revisit the problem of computing high-quality. alignment for literary works. We first perform a large-scale evaluation of automatic alignment for literary texts, which provides a fair assessment of the actual difficulty of this task. We then introduce a two-pass approach, based on a maximum entropy model. Experimental results for novels available in English and French or in English and Spanish demonstrate the effectiveness of our method.
This paper presents two improvements of language models based on Restricted Boltzmann Machine (RBM) for large machine translation tasks. In contrast to other continuous space approach, RBM based models can easily be integrated into the decoder and are able to directly learn a hidden representation of the n-gram. Previous work on RBM-based language models do not use a shared word representation and therefore, they might suffer of a lack of generalization for larger contexts. Moreover, since the training step is very time consuming, they are only used for quite small copora. In this work we add a shared word representation for the RBM-based language model by factorizing the weight matrix. In addition, we propose an efficient and tailored sampling algorithm that allows us to drastically speed up the training process. Experiments are carried out on two German to English translation tasks and the results show that the training time could be reduced by a factor of 10 without any drop in performance. Furthermore, the RBM-based model can also be trained on large size corpora.
This paper documents the systems developed by LIMSI for the IWSLT 2014 speech translation task (English→French). The main objective of this participation was twofold: adapting different components of the ASR baseline system to the peculiarities of TED talks and improving the machine translation quality on the automatic speech recognition output data. For the latter task, various techniques have been considered: punctuation and number normalization, adaptation to ASR errors, as well as the use of structured output layer neural network models for speech data.
In this paper we explore various adaptation techniques for continuous space translation models (CSTMs). We consider the following practical situation: given a large scale, state-of-the-art SMT system containing a CSTM, the task is to adapt the CSTM to a new domain using a (relatively) small in-domain parallel corpus. Our method relies on the definition of a new discriminative loss function for the CSTM that borrows from both the max-margin and pair-wise ranking approaches. In our experiments, the baseline out-of-domain SMT system is initially trained for the WMT News translation task, and the CSTM is to be adapted to the lecture translation task as defined by IWSLT evaluation campaign. Experimental results show that an improvement of 1.5 BLEU points can be achieved with the proposed adaptation method.
Statistical Machine Translation produces results that make it a competitive option in most machine-assisted translation scenarios. However, these good results often come at a very high computational cost and correspond to training regimes which are unfit to many practical contexts, where the ability to adapt to users and domains and to continuously integrate new data (eg. in post-edition contexts) are of primary importance. In this article, we show how these requirements can be met using a strategy for on-demand word alignment and model estimation. Most remarkably, our incremental system development framework is shown to deliver top quality translation performance even in the absence of tuning, and to surpass a strong baseline when performing online tuning. All these results obtained with great computational savings as compared to conventional systems.
In this paper, we present a freely available corpus of automatic translations accompanied with post-edited versions, annotated with labels identifying the different kinds of errors made by the MT system. These data have been extracted from translation students exercises that have been corrected by a senior professor. This corpus can be useful for training quality estimation tools and for analyzing the types of errors made MT system.
In Statistical Machine Translation (SMT), the constraints on word reorderings have a great impact on the set of potential translations that are explored. Notwithstanding computationnal issues, the reordering space of a SMT system needs to be designed with great care: if a larger search space is likely to yield better translations, it may also lead to more decoding errors, because of the added ambiguity and the interaction with the pruning strategy. In this paper, we study this trade-off using a state-of-the art translation system, where all reorderings are represented in a word lattice prior to decoding. This allows us to directly explore and compare different reordering spaces. We study in detail a rule-based preordering system, varying the length or number of rules, the tagset used, as well as contrasting with oracle settings and purely combinatorial subsets of permutations. We focus on two language pairs: English-French, a close language pair and English-German, known to be a more challenging reordering pair.
In this article, we present a sampling-based approach to improve bilingual sub-sentential alignment in parallel corpora. This approach can be used to align parallel sentences on an as needed basis, and is able to accurately align newly available sentences. We evaluate the resulting alignments on several Machine Translation tasks. Results show that for the tasks considered here, our approach performs on par with the state-of-the-art statistical alignment pipeline giza++/Moses, and obtains superior results in a number of configurations, notably when aligning additional parallel sentence pairs carefully selected to match the test input.
Arabic is a morphologically rich language, and Arabic texts abound of complex word forms built by concatenation of multiple subparts, corresponding for instance to prepositions, articles, roots prefixes, or suffixes. The development of Arabic Natural Language Processing applications, such as Machine Translation (MT) tools, thus requires some kind of morphological analysis. In this paper, we compare various strategies for performing such preprocessing, using generic machine learning techniques. The resulting tool is compared with two open domain alternatives in the context of a statistical MT task and is shown to be faster than its competitors, with no significant difference in MT quality.
Adaptation for Machine Translation has been studied in a variety of ways, using an ideal scenario where the training data can be split into ”out-of-domain” and ”in-domain” corpora, on which the adaptation is based. In this paper, we consider a more realistic setting which does not assume the availability of any kind of ”in-domain” data, hence the name ”any-text translation”. In this context, we present a new approach to contextually adapt a translation model onthe-fly, and present several experimental results where this approach outperforms conventionaly trained baselines. We also present a document-level contrastive evaluation whose results can be easily interpreted, even by non-specialists.
In Machine Translation, it is customary to compute the model score of a predicted hypothesis as a linear combination of multiple features, where each feature assesses a particular facet of the hypothesis. The choice of a linear combination is usually justified by the possibility of efficient inference (decoding); yet, the appropriateness of this simple combination scheme to the task at hand is rarely questioned. In this paper, we propose an approach that replaces the linear scoring function with a non-linear scoring function. To investigate the applicability of this approach, we rescore n-best lists generated with a conventional machine translation engine (using a linear scoring function for generating its hypotheses) with a non-linear scoring function learned using the learning-to-rank framework. Moderate, though consistent, gains in BLEU are demonstrated on the WMT’10, WMT’11 and WMT’12 test sets.
LIMSI took part in the IWSLT 2011 TED task in the MT track for English to French using the in-house n-code system, which implements the n-gram based approach to Machine Translation. This framework not only allows to achieve state-of-the-art results for this language pair, but is also appealing due to its conceptual simplicity and its use of well understood statistical language models. Using this approach, we compare several ways to adapt our existing systems and resources to the TED task with mixture of language models and try to provide an analysis of the modest gains obtained by training a log linear combination of inand out-of-domain models.
The Quaero program is an international project promoting research and industrial innovation on technologies for automatic analysis and classification of multimedia and multilingual documents. Within the program framework, research organizations and industrial partners collaborate to develop prototypes of innovating applications and services for access and usage of multimedia data. One of the topics addressed is the translation of spoken language. Each year, a project-internal evaluation is conducted by DGA to monitor the technological advances. This work describes the design and results of the 2011 evaluation campaign. The participating partners were RWTH, KIT, LIMSI and SYSTRAN. Their approaches are compared on both ASR output and reference transcripts of speech data for the translation between French and German. The results show that the developed techniques further the state of the art and improve translation quality.
We present a novel translation quality informed procedure for both extraction and scoring of phrase pairs in PBSMT systems. We reformulate the extraction problem in the supervised learning framework. Our goal is twofold. First, We attempt to take the translation quality into account; and second we incorporating arbitrary features in order to circumvent alignment errors. One-Class SVMs and the Mapping Convergence algorithm permit training a single-class classifier to discriminate between useful and useless phrase pairs. Such classifier can be learned from a training corpus that comprises only useful instances. The confidence score, produced by the classifier for each phrase pairs, is employed as a selection criteria. The smoothness of these scores allow a fine control over the size of the resulting translation model. Finally, confidence scores provide a new accuracy-based feature to score phrase pairs. Experimental evaluation of the method shows accurate assessments of phrase pairs quality even for regions in the space of possible phrase pairs that are ignored by other approaches. This enhanced evaluation of phrase pairs leads to improvements in the translation performance as measured by BLEU.
L’alignement sous-phrastique consiste à extraire des traductions d’unités textuelles de grain inférieur à la phrase à partir de textes multilingues parallèles alignés au niveau de la phrase. Un tel alignement est nécessaire, par exemple, pour entraîner des systèmes de traduction statistique. L’approche standard pour réaliser cette tâche implique l’estimation successive de plusieurs modèles probabilistes de complexité croissante et l’utilisation d’heuristiques qui permettent d’aligner des mots isolés, puis, par extension, des groupes de mots. Dans cet article, nous considérons une approche alternative, initialement proposée dans (Lardilleux & Lepage, 2008), qui repose sur un principe beaucoup plus simple, à savoir la comparaison des profils d’occurrences dans des souscorpus obtenus par échantillonnage. Après avoir analysé les forces et faiblesses de cette approche, nous montrons comment améliorer la détection d’unités de traduction longues, et évaluons ces améliorations sur des tâches de traduction automatique.
Dans les systèmes de traduction statistique à base de segments, le modèle de traduction est estimé à partir d’alignements mot-à-mot grâce à des heuristiques d’extraction et de valuation. Bien que ces alignements mot-à-mot soient construits par des modèles probabilistes, les processus d’extraction et de valuation utilisent ces modèles en faisant l’hypothèse que ces alignements sont déterministes. Dans cet article, nous proposons de lever cette hypothèse en considérant l’ensemble de la matrice d’alignement, d’une paire de phrases, chaque association étant valuée par sa probabilité. En comparaison avec les travaux antérieurs, nous montrons qu’en utilisant un modèle exponentiel pour estimer de manière discriminante ces probabilités, il est possible d’obtenir des améliorations significatives des performances de traduction. Ces améliorations sont mesurées à l’aide de la métrique BLEU sur la tâche de traduction de l’arabe vers l’anglais de l’évaluation NIST MT’09, en considérant deux types de conditions selon la taille du corpus de données parallèles utilisées.
The quality of statistical machine translation systems depends on the quality of the word alignments that are computed during the translation model training phase. IBM alignment models, as implemented in the GIZA++ toolkit, constitute the de facto standard for performing these computations. The resulting alignments and translation models are however very noisy, and several authors have tried to improve them. In this work, we propose a simple and effective approach, which considers alignment as a series of independent binary classification problems in the alignment matrix. Through extensive feature engineering and the use of stacking techniques, we were able to obtain alignments much closer to manually defined references than those obtained by the IBM models. These alignments also yield better translation models, delivering improved performance in a large scale Arabic to English translation task.
This paper advocates a complementary measure of translation performance that focuses on the constrastive ability of two or more systems or system versions to adequately translate source words. This is motivated by three main reasons : 1) existing automatic metrics sometimes do not show significant differences that can be revealed by fine-grained focussed human evaluation, 2) these metrics are based on direct comparisons between system hypotheses with the corresponding reference translations, thus ignoring the input words that were actually translated, and 3) as these metrics do not take input hypotheses from several systems at once, fine-grained contrastive evaluation can only be done indirectly. This proposal is illustrated on a multi-source Machine Translation scenario where multiple translations of a source text are available. Significant gains (up to +1.3 BLEU point) are achieved on these experiments, and contrastive lexical evaluation is shown to provide new information that can help to better analyse a system's performance.
Dans cet article, nous introduisons une méthode à base de règles permettant d’extraire automatiquement de l’historique des éditions de l’encyclopédie collaborative Wikipédia des corrections orthographiques. Cette méthode nous a permis de construire un corpus d’erreurs composé de 72 483 erreurs lexicales (non-word errors) et 74 100 erreurs grammaticales (real-word errors). Il n’existe pas, à notre connaissance, de plus gros corpus d’erreurs écologiques librement disponible. En outre, les techniques mises en oeuvre peuvent être facilement transposées à de nombreuses autres langues. La collecte de ce corpus ouvre de nouvelles perspectives pour l’étude des erreurs fréquentes ainsi que l’apprentissage et l’évaluation des correcteurs orthographiques automatiques. Plusieurs expériences illustrant son intérêt sont proposées.
This paper describes LIMSI’s Statistical Machine Translation systems (SMT) for the IWSLT evaluation, where we participated in two tasks (Talk for English to French and BTEC for Turkish to English). For the Talk task, we studied an extension of our in-house n-code SMT system (the integration of a bilingual reordering model over generalized translation units), as well as the use of training data extracted from Wikipedia in order to adapt the target language model. For the BTEC task, we concentrated on pre-processing schemes on the Turkish side in order to reduce the morphological discrepancies with the English side. We also evaluated the use of two different continuous space language models for such a small size of training data.
In this paper, we present the result of our work on improving the preprocessing for German-English statistical machine translation. We implemented and tested various improvements aimed at i) converting German texts to the new orthographic conventions; ii) performing a new tokenization for German; iii) normalizing lexical redundancy with the help of POS tagging and morphological analysis; iv) splitting German compound words with frequency based algorithm and; v) reducing singletons and out-of-vocabulary words. All these steps are performed during preprocessing on the German side. Combining all these processes, we reduced 10% of the singletons, 2% OOV words, and obtained 1.5 absolute (7% relative) BLEU improvement on the WMT 2010 German to English News translation task.
Les systèmes de traduction statistiques intègrent différents types de modèles dont les prédictions sont combinées, lors du décodage, afin de produire les meilleures traductions possibles. Traduire correctement des mots polysémiques, comme, par exemple, le mot avocat du français vers l’anglais (lawyer ou avocado), requiert l’utilisation de modèles supplémentaires, dont l’estimation et l’intégration s’avèrent complexes. Une alternative consiste à tirer parti de l’observation selon laquelle les ambiguïtés liées à la polysémie ne sont pas les mêmes selon les langues source considérées. Si l’on dispose, par exemple, d’une traduction vers l’espagnol dans laquelle avocat a été traduit par aguacate, alors la traduction de ce mot vers l’anglais n’est plus ambiguë. Ainsi, la connaissance d’une traduction français!espagnol permet de renforcer la sélection de la traduction avocado pour le système français!anglais. Dans cet article, nous proposons d’utiliser des documents en plusieurs langues pour renforcer les choix lexicaux effectués par un système de traduction automatique. En particulier, nous montrons une amélioration des performances sur plusieurs métriques lorsque les traductions auxiliaires utilisées sont obtenues manuellement.
Cet article présente une architecture inspirée des systèmes de reconnaissance vocale pour effectuer une normalisation orthographique de messages en « langage SMS ». Nous décrivons notre système de base, ainsi que diverses évolutions de ce système, qui permettent d’améliorer sensiblement la qualité des normalisations produites.
L’appariement d’entités nommées consiste à regrouper les différentes formes sous lesquelles apparaît une entité. Pour cela, des mesures de similarité textuelle sont généralement utilisées. Nous proposons de combiner plusieurs mesures afin d’améliorer les performances de la tâche d’appariement. À l’aide d’expériences menées sur deux corpus, nous montrons la pertinence de l’apprentissage supervisé dans ce but, particulièrement avec l’algorithme C4.5.
Dans ce travail, nous étudions en corpus la productivité quantitative des suffixations par -Able et par -ité du français, d’abord indépendamment l’une de l’autre, puis lorsqu’elles s’enchaînent dérivationnellement (la suffixation en -ité s’applique à des bases en -Able dans environ 15 % des cas). Nous estimons la productivité de ces suffixations au moyen de mesures statistiques dont nous suivons l’évolution par rapport à la taille du corpus. Ces deux suffixations sont productives en français moderne : elles forment de nouveaux lexèmes tout au long des corpus étudiés sans qu’on n’observe de saturation, leurs indices de productivité montrent une évolution stable bien qu’étant dépendante des calculs qui leur sont appliqués. On note cependant que, de façon générale, de ces deux suffixations, c’est la suffixation par -ité qui est la plus fréquente en corpus journalistique, sauf précisément quand -ité s’applique à un adjectif en -Able. Étant entendu qu’un adjectif en -Able et le nom en -ité correspondant expriment la même propriété, ce résultat indique que la complexité de la base est un paramètre à prendre en considération dans la formation du lexique possible.
L’apprentissage par analogie se fonde sur un principe inférentiel potentiellement pertinent pour le traitement des langues naturelles. L’utilisation de ce principe pour des tâches d’analyse linguistique présuppose toutefois une définition formelle de l’analogie entre séquences. Dans cet article, nous proposons une telle définition et montrons qu’elle donne lieu à l’implantation efficace d’un solveur d’équations analogiques sous la forme d’un transducteur fini. Munis de ces résultats, nous caractérisons empiriquement l’extension analogique de divers langages finis, correspondant à des dictionnaires de quatre langues.
Dans cet article, nous présentons une méthodologie d’apprentissage faiblement supervisé pour l’extraction automatique de paraphrases à partir du Web. À partir d’un seule exemple de paire (prédicat, arguments), un corpus est progressivement accumulé par sondage duWeb. Les phases de sondage alternent avec des phases de filtrage, durant lesquelles les paraphrases les moins plausibles sont éliminées à l’aide d’une procédure de clustering non supervisée. Ce mécanisme d’apprentissage s’appuie sur un système de Questions-Réponses existant et les paraphrases apprises seront utilisées pour en améliorer le rappel. Nous nous concentrons ici sur le mécanisme d’apprentissage de ce système et en présentons les premiers résultats.