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

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

Controlling Styles in Neural Machine Translation with Activation Prompt

Controlling styles in neural machine translation (NMT) has attracted wide attention, as it is crucial for enhancing user experience. Earlier studies on this topic typically concentrate on regulating the level of formality and achieve some progress in this area. However, they still encounter two major challenges. The first is the difficulty in style evaluation. The style comprises various aspects such as lexis, syntax, and others that provide abundant information. Nevertheless, only formality has been thoroughly investigated. The second challenge involves excessive dependence on incremental adjustments, particularly when new styles are necessary. To address both challenges, this paper presents a new benchmark and approach. A multiway stylized machine translation (MSMT) benchmark is introduced, incorporating diverse categories of styles across four linguistic domains. Then, we propose a method named style activation prompt (StyleAP) by retrieving prompts from stylized monolingual corpus, which does not require extra fine-tuning. Experiments show that StyleAP could effectively control the style of translation and achieve remarkable performance.Comment: Accepted by Findings of ACL 2023; The code is available at https://github.com/IvanWang0730/StyleA