Findings of the 2019 Conference on Machine Translation (WMT19)

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation

Learning multilingual and multimodal representations with language-specific encoders and decoders for machine translation

This thesis aims to study different language-specific approaches for Multilingual Machine Translation without parameter sharing and their properties compared to the current state-of-the-art based on parameter-sharing. We define Multilingual Machine Translation as the task that focuses on methods to translate between several pairs of languages in a single system. It has been widely studied in recent years due to its ability to easily scale to more languages, even between pairs never seen together during training (zero-shot translation). Several architectures have been proposed to tackle this problem with varying amounts of shared parameters between languages. Current state-of-the-art systems focus on a single sequence-to-sequence architecture where all languages share the complete set of parameters, including the token representation. While this has proven convenient for transfer learning, it makes it challenging to incorporate new languages into the trained model as all languages depend on the same parameters. What all proposed architectures have in common is enforcing a shared presentation space between languages. Specifically, during this work, we will employ as representation the final output of the encoders that the decoders will use to perform cross-attention. Having a shared space reduces noise as similar sentences at semantic level produce similar vectorial representations, helping the decoders process representations from several languages. This semantic representation is particularly important for zero-shot translation as the representation similarity to the languages pairs seen during training is key to reducing ambiguity between languages and obtaining good translation performance. This thesis is structured in three main blocks, focused on different scenarios of this task. Firstly, we propose a training method that enforces a common representation for bilingual training and a procedure to extend it to new languages efficiently. Secondly, we propose another training method that allows this representation to be learned directly on multilingual data and can be equally extended to new languages. Thirdly, we show that the proposed multilingual architecture is not limited only to textual languages. We extend our method to new data modalities by adding speech encoders, performing Spoken Language Translation, including Zero-Shot, to all the supported languages. Our main results show that the common intermediate representation is achievable in this scenario, matching the performance of previously shared systems while allowing the addition of new languages or data modalities efficiently without negative transfer learning to the previous languages or retraining the system.El objetivo de esta tesis es estudiar diferentes arquitecturas de Traducción Automática Multilingüe con parámetros específicos para cada idioma que no son compartidos, en contraposición al estado del arte actual basado en compartir parámetros. Podemos definir la Traducción Automática Multilingüe como la tarea que estudia métodos para traducir entre varios pares de idiomas en un único sistema. Ésta ha sido ampliamente estudiada en los últimos años debido a que nos permite escalar nuestros sistemas con facilidad a un gran número de idiomas, incluso entre pares de idiomas que no han sido nunca entrenados juntos (traducción zero-shot). Diversas arquitecturas han sido propuestas con diferentes niveles de parámetros compartidos entre idiomas, El estado del arte actual se enfoca hacía un solo modelo secuencia a secuencia donde todos los parámetros son compartidos por todos los idiomas, incluyendo la representación a nivel de unidad lingüística. Siendo esto beneficioso para la transferencia de conocimiento entre idiomas, también puede resultar una limitación a la hora de añadir nuevos, ya que modificaríamos los parámetros para todos los idiomas soportados. El elemento común de todas las arquitecturas propuestas es promover un espacio común donde representar a todos los idiomas en el sistema. Concretamente, durante este trabajo, nos referiremos a la representación final de los codificadores del sistema como este espacio, puesto que es la representación utilizada durante la atención cruzada por los decodificadores al generar traducciones. El objetivo de esta representación común es reducir ruido, ya que frases similares producirán representaciones similares, lo cual resulta de ayuda al usar un mismo decodificador para procesar la representación vectorial de varios idiomas. Esto es especialmente importante en el caso de la traducción zero-shot, ya que el par de idiomas no ha sido nunca entrenado conjuntamente, para reducir posibles ambigüedades y obtener una buena calidad de traducción. La tesis está organizada en tres bloques principales, enfocados en diferentes escenarios de esta tarea. Primero, proponemos un método para entrenar una representación común en sistemas bilingües, y un procedimiento para extenderla a nuevos idiomas de manera eficiente. Segundo, proponemos otro método de entrenamiento para aprender esta representación directamente desde datos multilingües y como puede ser igualmente extendida a nuevos idiomas. Tercero, mostramos que esta representación no está limitada únicamente a datos textuales. Para ello, extendemos nuestro método a otra modalidad de datos, en este caso discurso hablado, demostrando que podemos realizar traducción de audio a texto para todos los idiomas soportados, incluyendo traducción zero-shot. Nuestros resultados muestras que una representación común puede ser aprendida sin compartir parámetros entre idiomas, con una calidad de traducción similar a la del actual estado del arte, con la ventaja de permitirnos añadir nuevos idiomas o modalidades de datos de manera eficiente, sin transferencia negativa de conocimiento a los idiomas ya soportados y sin necesidad de reentrenarlos.Postprint (published version

Evaluating Multiway Multilingual NMT in the Turkic Languages

Despite the increasing number of large and comprehensive machine translation (MT) systems, evaluation of these methods in various languages has been restrained by the lack of high-quality parallel corpora as well as engagement with the people that speak these languages. In this study, we present an evaluation of state-of-the-art approaches to training and evaluating MT systems in 22 languages from the Turkic language family, most of which being extremely under-explored. First, we adopt the TIL Corpus with a few key improvements to the training and the evaluation sets. Then, we train 26 bilingual baselines as well as a multi-way neural MT (MNMT) model using the corpus and perform an extensive analysis using automatic metrics as well as human evaluations. We find that the MNMT model outperforms almost all bilingual baselines in the out-of-domain test sets and finetuning the model on a downstream task of a single pair also results in a huge performance boost in both low- and high-resource scenarios. Our attentive analysis of evaluation criteria for MT models in Turkic languages also points to the necessity for further research in this direction. We release the corpus splits, test sets as well as models to the public.Peer reviewe

A Large-Scale Study of Machine Translation in Turkic Languages

Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems. However, there is still a large number of languages that are yet to reap the benefits of NMT. In this paper, we provide the first large-scale case study of the practical application of MT in the Turkic language family in order to realize the gains of NMT for Turkic languages under high-resource to extremely low-resource scenarios. In addition to presenting an extensive analysis that identifies the bottlenecks towards building competitive systems to ameliorate data scarcity, our study has several key contributions, including, i) a large parallel corpus covering 22 Turkic languages consisting of common public datasets in combination with new datasets of approximately 1.4 million parallel sentences, ii) bilingual baselines for 26 language pairs, iii) novel high-quality test sets in three different translation domains and iv) human evaluation scores. All models, scripts, and data will be released to the public.Peer reviewe

Massively Multilingual Neural Machine Translation

Multilingual neural machine translation (NMT) enables training a single model that supports translation from multiple source languages into multiple target languages. In this paper, we push the limits of multilingual NMT in terms of number of languages being used. We perform extensive experiments in training massively multilingual NMT models, translating up to 102 languages to and from English within a single model. We explore different setups for training such models and analyze the trade-offs between translation quality and various modeling decisions. We report results on the publicly available TED talks multilingual corpus where we show that massively multilingual many-to-many models are effective in low resource settings, outperforming the previous state-of-the-art while supporting up to 59 languages. Our experiments on a large-scale dataset with 102 languages to and from English and up to one million examples per direction also show promising results, surpassing strong bilingual baselines and encouraging future work on massively multilingual NMT.Comment: Accepted as a long paper in NAACL 201