Machine Translation (MT) tools are widely used today, often in contexts where professional translators are not present. Despite progress in MT technology, a gap persists between system development and real-world usage, particularly for non-expert users who may struggle to assess translation reliability.This paper advocates for a human-centered approach to MT, emphasizing the alignment of system design with diverse communicative goals and contexts of use. We survey the literature in Translation Studies and Human-Computer Interaction to recontextualize MT evaluation and design to address the diverse real-world scenarios in which MT is used today.

In long-term translation projects, like Parliamentary text, there is a desire to build machine translation systems that can adapt to changes over time. We implement and examine a simple approach to continual learning for neural machine translation, exploring tradeoffs between consistency, the model’s ability to learn from incoming data, and the time a client would need to wait to obtain a newly trained translation system.

In September 2023, the Government of Canada issued a ‘Guide on the Use of Generative AI’ with recommendations for Canadian government institutions and their employees. As other similar documents published by various organizations in recent years, this document makes recommendations regarding transparency, stating that whenever generative AI is used to produce content, the reader should be informed that “messages addressed to them are generated by AI”. While this guide does not address specifically the case of machine translation, it does mention translation as a potential application of generative AI. Therefore, one question that naturally arises is: Should machine-translated texts be explicitly labelled as AI-generated content wherever they are used? In this position paper, we examine this question in detail, with the goal of proposing clear guidelines specifically regarding MT, not only for government institutions, but for anyone using MT technology to produce new versions of a text. Our main conclusion is that machine-translated text is indeed AI-generated content. As such, it should be explicitly marked everywhere it is used. We make recommendations as to what form this labelling might take. We also examine under what conditions labelling can be removed or omitted.

Incorporating terminology into a neural machine translation (NMT) system is a feature of interest for many users of machine translation. In this case study of English-French Canadian Parliamentary text, we examine the performance of standard NMT systems at handling terminology and consider the tradeoffs between potential performance improvements and the efforts required to maintain terminological resources specifically for NMT.

While recent studies have been dedicated to cleaning very noisy parallel corpora to improve Machine Translation training, we focus in this work on filtering a large and mostly clean Translation Memory. This problem of practical interest has not received much consideration from the community, in contrast with, for example, filtering large web-mined parallel corpora. We experiment with an extensive, multi-domain proprietary Translation Memory and compare five approaches involving deep-, feature-, and heuristic-based solutions. We propose two ways of evaluating this task, manual annotation and resulting Machine Translation quality. We report significant gains over a state-of-the-art, off-the-shelf cleaning system, using two MT engines.

We revisit the topic of translation direction in the data used for training neural machine translation systems and focusing on a real-world scenario with known translation direction and imbalances in translation direction: the Canadian Hansard. According to automatic metrics and we observe that using parallel data that was produced in the “matching” translation direction (Authentic source and translationese target) improves translation quality. In cases of data imbalance in terms of translation direction and we find that tagging of translation direction can close the performance gap. We perform a human evaluation that differs slightly from the automatic metrics and but nevertheless confirms that for this French-English dataset that is known to contain high-quality translations and authentic or tagged mixed source improves over translationese source for training.

Deep neural models tremendously improved machine translation. In this context, we investigate whether distinguishing machine from human translations is still feasible. We trained and applied 18 classifiers under two settings: a monolingual task, in which the classifier only looks at the translation; and a bilingual task, in which the source text is also taken into consideration. We report on extensive experiments involving 4 neural MT systems (Google Translate, DeepL, as well as two systems we trained) and varying the domain of texts. We show that the bilingual task is the easiest one and that transfer-based deep-learning classifiers perform best, with mean accuracies around 85% in-domain and 75% out-of-domain .

We present a fully unsupervised crosslingual semantic textual similarity (STS) metric, based on contextual embeddings extracted from BERT – Bidirectional Encoder Representations from Transformers (Devlin et al., 2019). The goal of crosslingual STS is to measure to what degree two segments of text in different languages express the same meaning. Not only is it a key task in crosslingual natural language understanding (XLU), it is also particularly useful for identifying parallel resources for training and evaluating downstream multilingual natural language processing (NLP) applications, such as machine translation. Most previous crosslingual STS methods relied heavily on existing parallel resources, thus leading to a circular dependency problem. With the advent of massively multilingual context representation models such as BERT, which are trained on the concatenation of non-parallel data from each language, we show that the deadlock around parallel resources can be broken. We perform intrinsic evaluations on crosslingual STS data sets and extrinsic evaluations on parallel corpus filtering and human translation equivalence assessment tasks. Our results show that the unsupervised crosslingual STS metric using BERT without fine-tuning achieves performance on par with supervised or weakly supervised approaches.

The WMT18 shared task on parallel corpus filtering (Koehn et al., 2018b) challenged teams to score sentence pairs from a large high-recall, low-precision web-scraped parallel corpus (Koehn et al., 2018a). Participants could use existing sample corpora (e.g. past WMT data) as a supervisory signal to learn what a “clean” corpus looks like. However, in lower-resource situations it often happens that the target corpus of the language is the only sample of parallel text in that language. We therefore made several unsupervised entries, setting ourselves an additional constraint that we not utilize the additional clean parallel corpora. One such entry fairly consistently scored in the top ten systems in the 100M-word conditions, and for one task—translating the European Medicines Agency corpus (Tiedemann, 2009)—scored among the best systems even in the 10M-word conditions.

We present our semantic textual similarity approach in filtering a noisy web crawled parallel corpus using YiSi—a novel semantic machine translation evaluation metric. The systems mainly based on this supervised approach perform well in the WMT18 Parallel Corpus Filtering shared task (4th place in 100-million-word evaluation, 8th place in 10-million-word evaluation, and 6th place overall, out of 48 submissions). In fact, our best performing system—NRC-yisi-bicov is one of the only four submissions ranked top 10 in both evaluations. Our submitted systems also include some initial filtering steps for scaling down the size of the test corpus and a final redundancy removal step for better semantic and token coverage of the filtered corpus. In this paper, we also describe our unsuccessful attempt in automatically synthesizing a noisy parallel development corpus for tuning the weights to combine different parallelism and fluency features.

Users of Statistical Machine Translation (SMT) sometimes turn to the Web to obtain data to train their systems. One problem with this approach is the potential for “MT contamination”: when large amounts of parallel data are collected automatically, there is a risk that a non-negligible portion consists of machine-translated text. Theoretically, using this kind of data to train SMT systems is likely to reinforce the errors committed by other systems, or even by an earlier versions of the same system. In this paper, we study the effect of MT-contaminated training data on SMT quality, by performing controlled simulations under a wide range of conditions. Our experiments highlight situations in which MT contamination can be harmful, and assess the potential of decontamination techniques.

We propose straightforward implementations of translation memory (TM) functionality for research purposes, using machine translation evaluation metrics as similarity functions. Experiments under various conditions demonstrate the effectiveness of the approach, but also highlight problems in evaluating the results using an MT evaluation methodology.

Cet article présente une méthode de traduction automatique statistique basée sur des segments non-continus, c’est-à-dire des segments formés de mots qui ne se présentent pas nécéssairement de façon contiguë dans le texte. On propose une méthode pour produire de tels segments à partir de corpus alignés au niveau des mots. On présente également un modèle de traduction statistique capable de tenir compte de tels segments, de même qu’une méthode d’apprentissage des paramètres du modèle visant à maximiser l’exactitude des traductions produites, telle que mesurée avec la métrique NIST. Les traductions optimales sont produites par le biais d’une recherche en faisceau. On présente finalement des résultats expérimentaux, qui démontrent comment la méthode proposée permet une meilleure généralisation à partir des données d’entraînement.

En dépit des travaux réalisés cette dernière décennie dans le cadre général de la traduction probabiliste, nous sommes toujours bien loin du jour où un engin de traduction automatique (probabiliste ou pas) sera capable de répondre pleinement aux besoins d’un traducteur professionnel. Dans une étude récente (Langlais, 2002), nous avons montré comment un engin de traduction probabiliste pouvait bénéficier de ressources terminologiques extérieures. Dans cette étude, nous montrons que les techniques de traduction probabiliste peuvent être utilisées pour extraire des informations sous-phrastiques d’une mémoire de traduction. Ces informations peuvent à leur tour s’avérer utiles à un engin de traduction probabiliste. Nous rapportons des résultats sur un corpus de test de taille importante en utilisant la mémoire de traduction d’un concordancier bilingue commercial.

We describe an experiment in rapid development of a statistical machine translation (SMT) system from scratch, using limited resources: under this heading we include not only training data, but also computing power, linguistic knowledge, programming effort, and absolute time.

Despite the exciting work accomplished over the past decade in the field of Statistical Machine Translation (SMT), we are still far from the point of being able to say that machine translation fully meets the needs of real-life users. In a previous study [6], we have shown how a SMT engine could benefit from terminological resources, especially when translating texts very different from those used to train the system. In the present paper, we discuss the opening of SMT to examples automatically extracted from a Translation Memory (TM). We report results on a fair-sized translation task using the database of a commercial bilingual concordancer.

L’utilité des outils d’aide à la traduction reposant sur les mémoires de traduction est souvent limitée par la nature des segments que celles-ci mettent en correspondance, le plus souvent des phrases entières. Cet article examine le potentiel d’un type de système qui serait en mesure de récupérer la traduction de séquences de mots de longueur arbitraire.

Translation memory systems (TMS) are a family of computer tools whose purpose is to facilitate and encourage the re-use of existing translations. By searching a database of past translations, these systems can retrieve the translation of whole segments of text and propose them to the translator for re-use. However, the usefulness of existing TMS’s is limited by the nature of the text segments that that they are able to put in correspondence, generally whole sentences. This article examines the potential of a type of system that is able to recuperate the translation of sub-sentential sequences of words.