Findings of the 2015 Workshop on Statistical Machine Translation

This paper presents the results of the WMT15 shared tasks, which included a standard news translation task, a metrics task, a tuning task, a task for run-time estimation of machine translation quality, and an automatic post-editing task. This year, 68 machine translation systems from 24 institutions were submitted to the ten translation directions in the standard translation task. An additional 7 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had three subtasks, with a total of 10 teams, submitting 34 entries. The pilot automatic postediting task had a total of 4 teams, submitting 7 entries

Findings of the 2014 Workshop on Statistical Machine Translation

This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation quality, and a metrics task. This year, 143 machine translation systems from 23 institutions were submitted to the ten translation directions in the standard translation task. An additional 6 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had four subtasks, with a total of 10 teams, submitting 57 entries

Findings of the 2016 Conference on Machine Translation.

This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments). The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries

Findings of the 2016 Conference on Machine Translation (WMT16)

This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments)

Apprentissage discriminant des modèles continus en traduction automatique

Over the past few years, neural network (NN) architectures have been successfully applied to many Natural Language Processing (NLP) applications, such as Automatic Speech Recognition (ASR) and Statistical Machine Translation (SMT).For the language modeling task, these models consider linguistic units (i.e words and phrases) through their projections into a continuous (multi-dimensional) space, and the estimated distribution is a function of these projections. Also qualified continuous-space models (CSMs), their peculiarity hence lies in this exploitation of a continuous representation that can be seen as an attempt to address the sparsity issue of the conventional discrete models. In the context of SMT, these echniques have been applied on neural network-based language models (NNLMs) included in SMT systems, and oncontinuous-space translation models (CSTMs). These models have led to significant and consistent gains in the SMT performance, but are also considered as very expensive in training and inference, especially for systems involving large vocabularies. To overcome this issue, Structured Output Layer (SOUL) and Noise Contrastive Estimation (NCE) have been proposed; the former modifies the standard structure on vocabulary words, while the latter approximates the maximum-likelihood estimation (MLE) by a sampling method. All these approaches share the same estimation criterion which is the MLE ; however using this procedure results in an inconsistency between theobjective function defined for parameter stimation and the way models are used in the SMT application. The work presented in this dissertation aims to design new performance-oriented and global training procedures for CSMs to overcome these issues. The main contributions lie in the investigation and evaluation of efficient training methods for (large-vocabulary) CSMs which aim~:(a) to reduce the total training cost, and (b) to improve the efficiency of these models when used within the SMT application. On the one hand, the training and inference cost can be reduced (using the SOUL structure or the NCE algorithm), or by reducing the number of iterations via a faster convergence. This thesis provides an empirical analysis of these solutions on different large-scale SMT tasks. On the other hand, we propose a discriminative training framework which optimizes the performance of the whole system containing the CSM as a component model. The experimental results show that this framework is efficient to both train and adapt CSM within SMT systems, opening promising research perspectives.Durant ces dernières années, les architectures de réseaux de neurones (RN) ont été appliquées avec succès à de nombreuses applications en Traitement Automatique de Langues (TAL), comme par exemple en Reconnaissance Automatique de la Parole (RAP) ainsi qu'en Traduction Automatique (TA).Pour la tâche de modélisation statique de la langue, ces modèles considèrent les unités linguistiques (c'est-à-dire des mots et des segments) à travers leurs projections dans un espace continu (multi-dimensionnel), et la distribution de probabilité à estimer est une fonction de ces projections.Ainsi connus sous le nom de "modèles continus" (MC), la particularité de ces derniers se trouve dans l'exploitation de la représentation continue qui peut être considérée comme une solution au problème de données creuses rencontré lors de l'utilisation des modèles discrets conventionnels.Dans le cadre de la TA, ces techniques ont été appliquées dans les modèles de langue neuronaux (MLN) utilisés dans les systèmes de TA, et dans les modèles continus de traduction (MCT).L'utilisation de ces modèles se sont traduit par d'importantes et significatives améliorations des performances des systèmes de TA. Ils sont néanmoins très coûteux lors des phrases d'apprentissage et d'inférence, notamment pour les systèmes ayant un grand vocabulaire.Afin de surmonter ce problème, l'architecture SOUL (pour "Structured Output Layer" en anglais) et l'algorithme NCE (pour "Noise Contrastive Estimation", ou l'estimation contrastive bruitée) ont été proposés: le premier modifie la structure standard de la couche de sortie, alors que le second cherche à approximer l'estimation du maximum de vraisemblance (MV) par une méthode d’échantillonnage.Toutes ces approches partagent le même critère d'estimation qui est la log-vraisemblance; pourtant son utilisation mène à une incohérence entre la fonction objectif définie pour l'estimation des modèles, et la manière dont ces modèles seront utilisés dans les systèmes de TA.Cette dissertation vise à concevoir de nouvelles procédures d'entraînement des MC, afin de surmonter ces problèmes.Les contributions principales se trouvent dans l'investigation et l'évaluation des méthodes d'entraînement efficaces pour MC qui visent à: (i) réduire le temps total de l'entraînement, et (ii) améliorer l'efficacité de ces modèles lors de leur utilisation dans les systèmes de TA.D'un côté, le coût d'entraînement et d'inférence peut être réduit (en utilisant l'architecture SOUL ou l'algorithme NCE), ou la convergence peut être accélérée.La dissertation présente une analyse empirique de ces approches pour des tâches de traduction automatique à grande échelle.D'un autre côté, nous proposons un cadre d'apprentissage discriminant qui optimise la performance du système entier ayant incorporé un modèle continu.Les résultats expérimentaux montrent que ce cadre d'entraînement est efficace pour l'apprentissage ainsi que pour l'adaptation des MC au sein des systèmes de TA, ce qui ouvre de nouvelles perspectives prometteuses

Sheffield systems for the English-Romanian translation task

© 2016 The Authors. Published by Association for Computational Linguistics. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: http://dx.doi.org/10.18653/v1/W16-2307This work was supported by the QT21 (H2020 No.645452) project

Comparison of Data Selection Techniques for the Translation of Video Lectures

[EN] For the task of online translation of scientific video lectures, using huge models is not possible. In order to get smaller and efficient models, we perform data selection. In this paper, we perform a qualitative and quantitative comparison of several data selection techniques, based on cross-entropy and infrequent n-gram criteria. In terms of BLEU, a combination of translation and language model cross-entropy achieves the most stable results. As another important criterion for measuring translation quality in our application, we identify the number of out-ofvocabulary words. Here, infrequent n-gram recovery shows superior performance. Finally, we combine the two selection techniques in order to benefit from both their strengths.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 287755 (transLectures), and the Spanish MINECO Active2Trans (TIN2012-31723) research project.Wuebker, J.; Ney, H.; Martínez-Villaronga, A.; Giménez Pastor, A.; Juan Císcar, A.; Servan, C.; Dymetman, M.... (2014). Comparison of Data Selection Techniques for the Translation of Video Lectures. Association for Machine Translation in the Americas. http://hdl.handle.net/10251/54431

Results of the WMT15 Metrics Shared Task

This paper presents the results of the WMT15 Metrics Shared Task. We asked participants of this task to score the outputs of the MT systems involved in the WMT15 Shared Translation Task. We collected scores of 46 metrics from 11 research groups. In addition to that, we computed scores of 7 standard metrics (BLEU, SentBLEU, NIST, WER, PER, TER and CDER) as baselines. The collected scores were evaluated in terms of system level correlation (how well each metric's scores correlate with WMT15 official manual ranking of systems) and in terms of segment level correlation (how often a metric agrees with humans in comparing two translations of a particular sentence)

Robust Text Correction for Grammar and Fluency

Grammar is one of the most important properties of natural language. It is a set of structural (i.e., syntactic and morphological) rules that are shared among native speakers in order to engage smooth communication. Automated grammatical error correction (GEC) is a natural language processing (NLP) application, which aims to correct grammatical errors in a given source sentence by computational models. Since the data-driven statistical methods began in 1990s and early 2000s, the GEC com- munity has worked on establishing a common framework for its evaluation (i.e., dataset and metric for benchmarking) in order to compare GEC models’ performance quantitatively. A series of shared tasks since early 2010s is a good example of this. In the first half of this thesis, I propose character-level and token-level error correction algorithms. For the character-level error correction, I introduce a semi-character recurrent neural network, which is motivated by a finding in psycholinguistics, called the Cmabrigde Uinervtisy (Cambridge University) effect or typoglycemia. For word-level error correc- tion, I propose an error-repair dependency parsing algorithm for ungrammatical texts. The algorithm can parse sentences and correct grammatical errors simultaneously. However, it is important to note that grammatical errors are not usually limited to mor- phological or syntactic errors. For example, collocational errors such as *quick/fast food and *fast/quick meal are not fully explained by only syntactic rules. This is another im- portant property of natural language, called fluency (or acceptability). Fluency is a level of mastery that goes beyond knowledge of how to follow the rules, and includes know- ing when they can be broken or flouted. In fact, the GEC community has also extended the scope of error types from closed class errors (e.g., noun numbers, verb forms) to the fluency-oriented errors. The second half of this thesis investigates GEC while considering fluency as well as grammaticality. When it comes to “whole-sentence” correction, by extending the scope of errors considering fluency as well as grammaticality, the GEC community has overlooked the reliability and validity of the task scheme (i.e., evaluation metric and dataset for bench- marking). Thus, I reassess the goals of GEC as a “whole-sentence” rewriting task while considering fluency. Following the fluency-oriented GEC framework, I introduce a new benchmark corpus that is more diverse in various aspects such as proficiency, topics, and learners’ native languages. Based on the fluency-oriented metric and dataset, I propose a new “whole-sentence” error correction model with neural reinforcement learning. Unlike conventional maximum likelihood estimation (MLE), the model directly optimizes toward an objective that consid- ers a sentence-level, task-specific evaluation metric. I demonstrate that the proposed model outperforms MLE in human and automated evaluation metrics. Finally, I conclude the thesis and outline ideas and suggestions for future GEC research