The paper presents a novel technique for speech translation using hierarchical phrased-based statistical machine translation (HPB-SMT). The system is based on translation of speech from phone sequences as opposed to conventional approach of speech translation from word sequences. The technique facilitates speech translation by allowing a machine translation (MT) system to access to phonetic information. This enables the MT system to act as both a word recognition and a translation component. This results in better performance than conventional speech translation approaches by recovering from recognition error with help of a source language model, translation model and target language model. For this purpose, the MT translation models are adopted to work on source language phones using a grapheme-to-phoneme component. The source-side phonetic confusions are handled using a confusion network. The result on IWLST'10 English- Chinese translation task shows a significant improvement in translation quality. In this paper, results for HPB-SMT are compared with previously published results of phrase-based statistical machine translation (PB-SMT) system (Baseline). The HPB-SMT system outperforms PB-SMT in this regard.

Aligning a sequence of words to one of its infrequent translations is a difficult task. We propose a simple and original solution to this problem that yields to significant gains over a state-of-the-art transpotting task. Our approach consists in aligning non parallel sentences from the training data in order to reinforce online the alignment models. We show that using only a few pairs of non parallel sentences allows to improve significantly the alignment of infrequent translations.

Discriminative training for MT usually involves numerous features and requires large-scale training set to reach reliable parameter estimation. Other than using the expensive human-labeled parallel corpora for training, semi-supervised methods have been proposed to generate huge amount of “hallucinated” data which relieves the data sparsity problem. However the large training set contains both good samples which are suitable for training and bad ones harmful to the training. How to select training samples from vast amount of data can greatly affect the training performance. In this paper we propose a method for selecting samples that are most suitable for discriminative training according to a criterion measuring the dataset quality. Our experimental results show that by adding samples to the training set selectively, we are able to exceed the performance of system trained with the same amount of samples selected randomly.

In this paper, we introduce a simple technique for incorporating domain information into a statistical machine translation system that significantly improves translation quality when test data comes from multiple domains. Our approach augments (conjoins) standard translation model and language model features with domain indicator features and requires only minimal modifications to the optimization and decoding procedures. We evaluate our method on two language pairs with varying numbers of domains, and observe significant improvements of up to 1.0 BLEU.

This paper defines a method for lexicon in the biomedical domain from comparable corpora. The method is based on compositional translation and exploits morpheme-level translation equivalences. It can generate translations for a large variety of morphologically constructed words and can also generate ’fertile’ translations. We show that fertile translations increase the overall quality of the extracted lexicon for English to French translation.

As machine translation quality continues to improve, the idea of using MT to assist human translators becomes increasingly attractive. In this work, we discuss and provide empirical evidence of the challenges faced when adapting traditional MT systems to provide automatic translations for human post-editors to correct. We discuss the differences between this task and traditional adequacy-based tasks and the challenges that arise when using automatic metrics to predict the amount of effort required to post-edit translations. A series of experiments simulating a real-world localization scenario shows that current metrics under-perform on this task, even when tuned to maximize correlation with expert translator judgments, illustrating the need to rethink traditional MT pipelines when addressing the challenges of this translation task.

When parallel or comparable corpora are harvested from the web, there is typically a tradeoff between the size and quality of the data. In order to improve quality, corpus collection efforts often attempt to fix or remove misaligned sentence pairs. But, at the same time, Statistical Machine Translation (SMT) systems are widely assumed to be relatively robust to sentence alignment errors. However, there is little empirical evidence to support and characterize this robustness. This contribution investigates the impact of sentence alignment errors on a typical phrase-based SMT system. We confirm that SMT systems are highly tolerant to noise, and that performance only degrades seriously at very high noise levels. Our findings suggest that when collecting larger, noisy parallel data for training phrase-based SMT, cleaning up by trying to detect and remove incorrect alignments can actually degrade performance. Although fixing errors, when applicable, is a preferable strategy to removal, its benefits only become apparent for fairly high misalignment rates. We provide several explanations to support these findings.

In this paper, we investigate large-scale lightly-supervised training with a pivot language: We augment a baseline statistical machine translation (SMT) system that has been trained on human-generated parallel training corpora with large amounts of additional unsupervised parallel data; but instead of creating this synthetic data from monolingual source language data with the baseline system itself, or from target language data with a reverse system, we employ a parallel corpus of target language data and data in a pivot language. The pivot language data is automatically translated into the source language, resulting in a trilingual corpus with unsupervised source language side. We augment our baseline system with the unsupervised source-target parallel data. Experiments are conducted for the German-French language pair using the standard WMT newstest sets for development and testing. We obtain the unsupervised data by translating the English side of the English-French 109 corpus to German. With careful system design, we are able to achieve improvements of up to +0.4 points BLEU / -0.7 points TER over the baseline.

We propose interpolated backoff methods to strike the balance between traditional surface form translation models and factored models that decompose translation into lemma and morphological feature mapping steps. We show that this approach improves translation quality by 0.5 BLEU (German–English) over phrase-based models, due to the better translation of rare nouns and adjectives.

In this paper, we discuss the development of statistical machine translation for English to/from White Hmong (Language code: mww). White Hmong is a Hmong-Mien language, originally spoken mostly in Southeast Asia, but now predominantly spoken by a large diaspora throughout the world, with populations in the United States, Australia, France, Thailand and elsewhere. Building statistical translation systems for Hmong proved to be incredibly challenging since there are no known parallel or monolingual corpora for the language; in fact, finding data for Hmong proved to be one of the biggest challenges to getting the project off the ground. It was only through a close collaboration with the Hmong community, and active and tireless participation of Hmong speakers, that it became possible to build up a critical mass of data to make the translation project a reality. We see this effort as potentially replicable for other severely resource poor languages of the world, which is likely the case for the majority of the languages still spoken on the planet. Further, the work here suggests that research and work on other severely under-resourced languages can have significant positive impacts for the affected communities, both for accessibility and language preservation.

In this paper, we propose a novel lattice-based MT combination methodology that we call Target-to-Target Decoding (TTD). The combination process is carried out as a “translation” from backbone to the combination result. This perspective suggests the use of existing phrase-based MT techniques in the combination framework. We show how phrase extraction rules and confidence estimations inspired from machine translation improve results. We also propose system-specific LMs for estimating N-gram consensus. Our results show that our approach yields a strong improvement over the best single MT system and competes with other state-of-the-art combination systems.

In an ideal cross-lingual information retrieval (CLIR) system, a user query would generate a search over documents in a different language and the relevant results would be presented in the user’s language. In practice, CLIR systems are typically evaluated by judging result relevance in the document language, to factor out the effects of translating the results using machine translation (MT). In this paper, we investigate the influence of four different approaches for integrating MT and CLIR on both retrieval accuracy and user judgment of relevancy. We create a corpus with relevance judgments for both human and machine translated results, and use it to quantify the effect that MT quality has on end-to-end relevance. We find that MT errors result in a 16-39% decrease in mean average precision over the ground truth system that uses human translations. MT errors also caused relevant sentences to appear irrelevant – 5-19% of sentences were relevant in human translation, but were judged irrelevant in MT. To counter this degradation, we present two hybrid retrieval models and two automatic MT post-editing techniques and show that these approaches substantially mitigate the errors and improve the end-to-end relevance.

We present a detailed analysis of a graph-based annotation strategy that we employed to annotate a corpus of 11,292 real-world English to Spanish automatic translations with relative (ranking) and absolute (adequate/non-adequate) quality assessments. The proposed approach, inspired by previous work in Interactive Evolutionary Computation and Interactive Genetic Algorithms, results in a simpler and faster annotation process. We empirically compare the method against a traditional, explicit ranking approach, and show that the graph-based strategy: 1) is considerably faster, and 2) produces consistently more reliable annotations.

Viewing machine translation as a structured classification problem has provided a gateway for a host of structured prediction techniques to enter the field. In particular, large-margin structured prediction methods for discriminative training of feature weights, such as the structured perceptron or MIRA, have started to match or exceed the performance of existing methods such as MERT. One issue with structured problems in general is the difficulty in obtaining fully structured labels, e.g., in machine translation, obtaining reference translations or parallel sentence corpora for arbitrary language pairs. Another issue, more specific to the translation domain, is the difficulty in online training of machine translation systems, since existing methods often require bilingual knowledge to correct translation output online. We propose a solution to these two problems, by demonstrating a way to incorporate binary-labeled feedback (i.e., feedback on whether a translation hypothesis is a “good” or understandable one or not), a form of supervision that can be easily integrated in an online manner, into a machine translation framework. Experimental results show marked improvement by incorporating binary feedback on unseen test data, with gains exceeding 5.5 BLEU points.

The IBM schemes use weighted cooccurrence counts to iteratively improve translation and alignment probability estimates. We argue that: 1) these cooccurrence counts should be combined differently to capture word correlation; 2) alignment probabilities adopt predictable distributions; and 3) consequently, no iteration is needed. This applies equally well to word-based and phrase-based approaches. The resulting scheme, dubbed HAL, outperforms the IBM scheme in experiments.

This paper introduces two novel approaches for extracting compact grammars for hierarchical phrase-based translation. The first is a combinatorial optimization approach and the second is a Bayesian model over Hiero grammars using Variational Bayes for inference. In contrast to the conventional Hiero (Chiang, 2007) rule extraction algorithm , our methods extract compact models reducing model size by 17.8% to 57.6% without impacting translation quality across several language pairs. The Bayesian model is particularly effective for resource-poor languages with evidence from Korean-English translation. To our knowledge, this is the first alternative to Hiero-style rule extraction that finds a more compact synchronous grammar without hurting translation performance.

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.

We present a simple and effective infrastructure for domain adaptation for statistical machine translation (MT). To build MT systems for different domains, it trains, tunes and deploys a single translation system that is capable of producing adapted domain translations and preserving the original generic accuracy at the same time. The approach unifies automatic domain detection and domain model parameterization into one system. Experiment results on 20 language pairs demonstrate its viability.

This paper gives a detailed analysis of different approaches to adapt a statistical machine translation system towards a target domain using small amounts of parallel in-domain data. Therefore, we investigate the differences between the approaches addressing adaptation on the two main steps of building a translation model: The candidate selection and the phrase scoring. For the latter step we characterized the differences by four key aspects. We performed experiments on two different tasks of speech translation and analyzed the influence of the different aspects on the overall translation quality. On both tasks we could show significant improvements by using the presented adaptation techniques.

This paper shows how the disambiguation of discourse connectives can improve their automatic translation, while preserving the overall performance of statistical MT as measured by BLEU. State-of-the-art automatic classifiers for rhetorical relations are used prior to MT to label discourse connectives that signal those relations. These labels are used for MT in two ways: (1) by augmenting factored translation models; and (2) by using the probability distributions of labels in order to train and tune SMT. The improvement of translation quality is demonstrated using a new semi-automated metric for discourse connectives, on the English/French WMT10 data, while BLEU scores remain comparable to non-discourse-aware systems, due to the low frequency of discourse connectives.

The standard procedure to train the translation model of a phrase-based SMT system is to concatenate all available parallel data, to perform word alignment, to extract phrase pairs and to calculate translation probabilities by simple relative frequency. However, parallel data is quite inhomogeneous in many practical applications with respect to several factors like data source, alignment quality, appropriateness to the task, etc. We propose a general framework to take into account these factors during the calculation of the phrase-table, e.g. by better distributing the probability mass of the individual phrase pairs. No additional feature functions are needed. We report results on two well-known tasks: the IWSLT’11 and WMT’11 evaluations, in both conditions translating from English to French. We give detailed results for different functions to weight the bitexts. Our best systems improve a strong baseline by up to one BLEU point without any impact on the computational complexity during training or decoding.

This paper addresses the problem of reliably measuring productivity gains by professional translators working with a machine translation enhanced computer assisted translation tool. In particular, we report on a field test we carried out with a commercial CAT tool in which translation memory matches were supplemented with suggestions from a commercial machine translation engine. The field test was conducted with 12 professional translators working on real translation projects. Productivity of translators were measured with two indicators, post-editing speed and post-editing effort, on two translation directions, English–Italian and English–German, and two linguistic domains, legal and information technology. Besides a detailed statistical analysis of the experimental results, we also discuss issues encountered in running the test.

We present an approach for Hybrid Machine Translation, based on a Machine-Learning framework. Our method combines output from several source systems. We first define an extensible, total order on translations and use it to estimate a ranking on the sentence level for a given set of systems. We introduce and define the notion of joint, binarised feature vectors. We train an SVM-based classifier and show how its classification results can be used to create hybrid translations. We describe a series of oracle experiments on data sets from the WMT11 translation task in order to find an upper bound regarding the achievable level of translation quality. We also present results from first experiments with an implemented version of our system. Evaluation using NIST and BLEU metrics indicates that the proposed method can outperform its individual source systems. An interesting finding is that our approach allows to leverage good translations from otherwise bad systems as the translation quality estimation is based on sentence-level phenomena rather than corpus-level metrics. We conclude by summarising our findings and by giving an outlook to future work.

This paper investigates the usefulness of automatic machine translation metrics when analyzing the impact of source reformulations on the quality of machine-translated user generated content. We propose a novel framework to quickly identify rewriting rules which improve or degrade the quality of MT output, by trying to rely on automatic metrics rather than human judgments. We find that this approach allows us to quickly identify overlapping rules between two language pairs (English- French and English-German) and specific cases where the rules’ precision could be improved.

Mismatches between training and test data are a ubiquitous problem for real SMT applications. In this paper, we examine a type of mismatch that commonly arises when translating from French and similar languages: available training data is mostly formal register, but test data may well be informal register. We consider methods for defining surface transformations that map common informal language constructions into their formal language counterparts, or vice versa; we then describe two ways to use these mappings, either to create artificial training data or to pre-process source text at run-time. An initial evaluation performed using crowd-sourced comparisons of alternate translations produced by a French-to-English SMT system suggests that both methods can improve performance, with run-time pre-processing being the more effective of the two.

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.

This paper describes a range of automatic and manual comparisons of phrase-based and syntax-based statistical machine translation methods applied to English-German and English-French translation of user-generated content. The syntax-based methods underperform the phrase-based models and the relaxation of syntactic constraints to broaden translation rule coverage means that these models do not necessarily generate output which is more grammatical than the output produced by the phrase-based models. Although the systems generate different output and can potentially be fruitfully combined, the lack of systematic difference between these models makes the combination task more challenging.

The technique of pruning phrase tables that are used for statistical machine translation (SMT) can achieve substantial reductions in bulk and improve translation quality, especially for very large corpora such at the Giga-FrEn. This can be further improved by conditioning each significance test on other phrase pair co-occurrence counts resulting in an additional reduction in size and increase in BLEU score. A series of experiments using Moses and the WMT11 corpora for French to English have been performed to quantify the improvement. By adhering strictly to the recommendations for the WMT11 baseline system, a strong reproducible research baseline was employed.

Most attempts at integrating word sense disambiguation with statistical machine translation have focused on supervised disambiguation approaches. These approaches are of limited use when the distribution of the test data differs strongly from that of the training data; however, word sense errors tend to be especially common under these conditions. In this paper we present different approaches to unsupervised word translation disambiguation and apply them to the problem of translating conversational speech under resource-poor training conditions. Both human and automatic evaluation metrics demonstrate significant improvements resulting from our technique.