Lessons learned in multilingual grounded language learning

Recent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language. Here, we investigate in detail which conditions affect the performance of this type of grounded language learning model. We show that multilingual training improves over bilingual training, and that low-resource languages benefit from training with higher-resource languages. We demonstrate that a multilingual model can be trained equally well on either translations or comparable sentence pairs, and that annotating the same set of images in multiple language enables further improvements via an additional caption-caption ranking objective.Comment: CoNLL 201

A Systematic Study of Inner-Attention-Based Sentence Representations in Multilingual Neural Machine Translation

Neural machine translation has considerably improved the quality of automatic translations by learning good representations of input sentences. In this article, we explore a multilingual translation model capable of producing fixed-size sentence representations by incorporating an intermediate crosslingual shared layer, which we refer to as attention bridge. This layer exploits the semantics from each language and develops into a language-agnostic meaning representation that can be efficiently used for transfer learning. We systematically study the impact of the size of the attention bridge and the effect of including additional languages in the model. In contrast to related previous work, we demonstrate that there is no conflict between translation performance and the use of sentence representations in downstream tasks. In particular, we show that larger intermediate layers not only improve translation quality, especially for long sentences, but also push the accuracy of trainable classification tasks. Nevertheless, shorter representations lead to increased compression that is beneficial in non-trainable similarity tasks. Similarly, we show that trainable downstream tasks benefit from multilingual models, whereas additional language signals do not improve performance in non-trainable benchmarks. This is an important insight that helps to properly design models for specific applications. Finally, we also include an in-depth analysis of the proposed attention bridge and its ability to encode linguistic properties. We carefully analyze the information that is captured by individual attention heads and identify interesting patterns that explain the performance of specific settings in linguistic probing tasks.Peer reviewe

Symbolic inductive bias for visually grounded learning of spoken language

A widespread approach to processing spoken language is to first automatically transcribe it into text. An alternative is to use an end-to-end approach: recent works have proposed to learn semantic embeddings of spoken language from images with spoken captions, without an intermediate transcription step. We propose to use multitask learning to exploit existing transcribed speech within the end-to-end setting. We describe a three-task architecture which combines the objectives of matching spoken captions with corresponding images, speech with text, and text with images. We show that the addition of the speech/text task leads to substantial performance improvements on image retrieval when compared to training the speech/image task in isolation. We conjecture that this is due to a strong inductive bias transcribed speech provides to the model, and offer supporting evidence for this.Comment: ACL 201

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

Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddings

Inducing semantic representations directly from speech signals is a highly challenging task but has many useful applications in speech mining and spoken language understanding. This study tackles the unsupervised learning of semantic representations for spoken utterances. Through converting speech signals into hidden units generated from acoustic unit discovery, we propose WavEmbed, a multimodal sequential autoencoder that predicts hidden units from a dense representation of speech. Secondly, we also propose S-HuBERT to induce meaning through knowledge distillation, in which a sentence embedding model is first trained on hidden units and passes its knowledge to a speech encoder through contrastive learning. The best performing model achieves a moderate correlation (0.5~0.6) with human judgments, without relying on any labels or transcriptions. Furthermore, these models can also be easily extended to leverage textual transcriptions of speech to learn much better speech embeddings that are strongly correlated with human annotations. Our proposed methods are applicable to the development of purely data-driven systems for speech mining, indexing and search

Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning

Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.This work has been partially supported by the European Commission ICT COST Action “Multi-task, Multilingual, Multi-modal Language Generation” (CA18231). AE was supported by BAGEP 2021 Award of the Science Academy. EE was supported in part by TUBA GEBIP 2018 Award. BP is in in part funded by Independent Research Fund Denmark (DFF) grant 9063-00077B. IC has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838188. EL is partly funded by Generalitat Valenciana and the Spanish Government throught projects PROMETEU/2018/089 and RTI2018-094649-B-I00, respectively. SMI is partly funded by UNIRI project uniri-drustv-18-20. GB is partly supported by the Ministry of Innovation and the National Research, Development and Innovation Office within the framework of the Hungarian Artificial Intelligence National Laboratory Programme. COT is partially funded by the Romanian Ministry of European Investments and Projects through the Competitiveness Operational Program (POC) project “HOLOTRAIN” (grant no. 29/221 ap2/07.04.2020, SMIS code: 129077) and by the German Academic Exchange Service (DAAD) through the project “AWAKEN: content-Aware and netWork-Aware faKE News mitigation” (grant no. 91809005). ESA is partially funded by the German Academic Exchange Service (DAAD) through the project “Deep-Learning Anomaly Detection for Human and Automated Users Behavior” (grant no. 91809358)