Word alignment and smoothing methods in statistical machine translation: Noise, prior knowledge and overfitting

This thesis discusses how to incorporate linguistic knowledge into an SMT system. Although one important category of linguistic knowledge is that obtained by a constituent / dependency parser, a POS / super tagger, and a morphological analyser, linguistic knowledge here includes larger domains than this: Multi-Word Expressions, Out-Of-Vocabulary words, paraphrases, lexical semantics (or non-literal translations), named-entities, coreferences, and transliterations. The first discussion is about word alignment where we propose a MWE-sensitive word aligner. The second discussion is about the smoothing methods for a language model and a translation model where we propose a hierarchical Pitman-Yor process-based smoothing method. The common grounds for these discussion are the examination of three exceptional cases from real-world data: the presence of noise, the availability of prior knowledge, and the problem of underfitting. Notable characteristics of this design are the careful usage of (Bayesian) priors in order that it can capture both frequent and linguistically important phenomena. This can be considered to provide one example to solve the problems of statistical models which often aim to learn from frequent examples only, and often overlook less frequent but linguistically important phenomena

A Text Rewriting Decoder with Application to Machine Translation

Ph.DDOCTOR OF PHILOSOPH

Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task

Previously, neural methods in grammatical error correction (GEC) did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) baselines. We demonstrate parallels between neural GEC and low-resource neural MT and successfully adapt several methods from low-resource MT to neural GEC. We further establish guidelines for trustable results in neural GEC and propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. Proposed methods include adding source-side noise, domain-adaptation techniques, a GEC-specific training-objective, transfer learning with monolingual data, and ensembling of independently trained GEC models and language models. The combined effects of these methods result in better than state-of-the-art neural GEC models that outperform previously best neural GEC systems by more than 10% M$^2$ on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set. Non-neural state-of-the-art systems are outperformed by more than 2% on the CoNLL-2014 benchmark and by 4% on JFLEG.Comment: Accepted for oral presentation in long paper research track at NAACL 201

Domain adaptation for neural machine translation

The development of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time. However, such translation models struggle when translating text of a specific domain. A domain may consist of text on a well-defined topic, or text of unknown provenance with an identifiable vocabulary distribution, or language with some other stylometric feature. While NMT models can achieve good translation performance on domain-specific data via simple tuning on a representative training corpus, such data-centric approaches have negative side-effects. These include over-fitting, brittleness, and `catastrophic forgetting' of previous training examples. In this thesis we instead explore more robust approaches to domain adaptation for NMT. We consider the case where a system is adapted to a specified domain of interest, but may also need to accommodate new language, or domain-mismatched sentences. We explore techniques relating to data selection and curriculum, model parameter adaptation procedure, and inference procedure. We show that iterative fine-tuning can achieve strong performance over multiple related domains, and that Elastic Weight Consolidation can be used to mitigate catastrophic forgetting in NMT domain adaptation across multiple sequential domains. We develop a robust variant of Minimum Risk Training which allows more beneficial use of small, highly domain-specific tuning sets than simple cross-entropy fine-tuning, and can mitigate exposure bias resulting from domain over-fitting. We extend Bayesian Interpolation inference schemes to Neural Machine Translation, allowing adaptive weighting of NMT ensembles to translate text from an unknown domain. Finally we demonstrate the benefit of multi-domain adaptation approaches for other lines of NMT research. We show that NMT systems using multiple forms of data representation can benefit from multi-domain inference approaches. We also demonstrate a series of domain adaptation approaches to mitigating the effects of gender bias in machine translation

Attelage de systèmes de transcription automatique de la parole

Nous abordons, dans cette thèse, les méthodes de combinaison de systèmesde transcription de la parole à Large Vocabulaire. Notre étude se concentre surl attelage de systèmes de transcription hétérogènes dans l objectif d améliorerla qualité de la transcription à latence contrainte. Les systèmes statistiquessont affectés par les nombreuses variabilités qui caractérisent le signal dela parole. Un seul système n est généralement pas capable de modéliserl ensemble de ces variabilités. La combinaison de différents systèmes detranscription repose sur l idée d exploiter les points forts de chacun pourobtenir une transcription finale améliorée. Les méthodes de combinaisonproposées dans la littérature sont majoritairement appliquées a posteriori,dans une architecture de transcription multi-passes. Cela nécessite un tempsde latence considérable induit par le temps d attente requis avant l applicationde la combinaison.Récemment, une méthode de combinaison intégrée a été proposée. Cetteméthode est basée sur le paradigme de décodage guidé (DDA :Driven DecodingAlgorithm) qui permet de combiner différents systèmes durant le décodage. Laméthode consiste à intégrer des informations en provenance de plusieurs systèmes dits auxiliaires dans le processus de décodage d un système dit primaire.Notre contribution dans le cadre de cette thèse porte sur un double aspect : d une part, nous proposons une étude sur la robustesse de la combinaison par décodage guidé. Nous proposons ensuite, une amélioration efficacement généralisable basée sur le décodage guidé par sac de n-grammes,appelé BONG. D autre part, nous proposons un cadre permettant l attelagede plusieurs systèmes mono-passe pour la construction collaborative, à latenceréduite, de la sortie de l hypothèse de reconnaissance finale. Nous présentonsdifférents modèles théoriques de l architecture d attelage et nous exposons unexemple d implémentation en utilisant une architecture client/serveur distribuée. Après la définition de l architecture de collaboration, nous nous focalisons sur les méthodes de combinaison adaptées à la transcription automatiqueà latence réduite. Nous proposons une adaptation de la combinaison BONGpermettant la collaboration, à latence réduite, de plusieurs systèmes mono-passe fonctionnant en parallèle. Nous présentons également, une adaptationde la combinaison ROVER applicable durant le processus de décodage via unprocessus d alignement local suivi par un processus de vote basé sur la fréquence d apparition des mots. Les deux méthodes de combinaison proposéespermettent la réduction de la latence de la combinaison de plusieurs systèmesmono-passe avec un gain significatif du WER.This thesis presents work in the area of Large Vocabulary ContinuousSpeech Recognition (LVCSR) system combination. The thesis focuses onmethods for harnessing heterogeneous systems in order to increase theefficiency of speech recognizer with reduced latency.Automatic Speech Recognition (ASR) is affected by many variabilitiespresent in the speech signal, therefore single ASR systems are usually unableto deal with all these variabilities. Considering these limitations, combinationmethods are proposed as alternative strategies to improve recognitionaccuracy using multiple recognizers developed at different research siteswith different recognition strategies. System combination techniques areusually used within multi-passes ASR architecture. Outputs of two or moreASR systems are combined to estimate the most likely hypothesis amongconflicting word pairs or differing hypotheses for the same part of utterance.The contribution of this thesis is twofold. First, we study and analyze theintegrated driven decoding combination method which consists in guidingthe search algorithm of a primary ASR system by the one-best hypothesesof auxiliary systems. Thus we propose some improvements in order to makethe driven decoding more efficient and generalizable. The proposed methodis called BONG and consists in using Bag Of N-Gram auxiliary hypothesisfor the driven decoding.Second, we propose a new framework for low latency paralyzed single-passspeech recognizer harnessing. We study various theoretical harnessingmodels and we present an example of harnessing implementation basedon client/server distributed architecture. Afterwards, we suggest differentcombination methods adapted to the presented harnessing architecture:first we extend the BONG combination method for low latency paralyzedsingle-pass speech recognizer systems collaboration. Then we propose, anadaptation of the ROVER combination method to be performed during thedecoding process using a local vote procedure followed by voting based onword frequencies.LE MANS-BU Sciences (721812109) / SudocSudocFranceF

Intégration du contexte en traduction statistique à l’aide d’un perceptron à plusieurs couches

Les systèmes de traduction statistique à base de segments traduisent les phrases un segment à la fois, en plusieurs étapes. À chaque étape, ces systèmes ne considèrent que très peu d’informations pour choisir la traduction d’un segment. Les scores du dictionnaire de segments bilingues sont calculés sans égard aux contextes dans lesquels ils sont utilisés et les modèles de langue ne considèrent que les quelques mots entourant le segment traduit.Dans cette thèse, nous proposons un nouveau modèle considérant la phrase en entier lors de la sélection de chaque mot cible. Notre modèle d’intégration du contexte se différentie des précédents par l’utilisation d’un ppc (perceptron à plusieurs couches). Une propriété intéressante des ppc est leur couche cachée, qui propose une représentation alternative à celle offerte par les mots pour encoder les phrases à traduire. Une évaluation superficielle de cette représentation alter- native nous a montré qu’elle est capable de regrouper certaines phrases sources similaires même si elles étaient formulées différemment. Nous avons d’abord comparé avantageusement les prédictions de nos ppc à celles d’ibm1, un modèle couramment utilisé en traduction. Nous avons ensuite intégré nos ppc à notre système de traduction statistique de l’anglais vers le français. Nos ppc ont amélioré les traductions de notre système de base et d’un deuxième système de référence auquel était intégré IBM1.Phrase-based statistical machine translation systems translate source sentences one phrase at a time, conditioning the choice of each phrase on very little information. Bilingual phrase table scores are computed regardless of the context in which the phrases are used and language models only look at few words surrounding the target phrases. In this thesis, we propose a novel model to predict words that should appear in a translation given the source sentence as a whole. Our model differs from previous works by its use of mlp (multilayer perceptrons). Our interest in mlp lies in their hidden layer that encodes source sentences in a representation that is only loosely tied to words. We observed that this hidden layer was able to cluster some sentences having similar translations even if they were formulated differently. In a first set of experiments, we compared favorably our mlp to ibm1, a well known model in statistical machine translation. In a second set of experiments, we embedded our ppc in our English to French statistical machine translation system. Our MLP improved translations quality over our baseline system and a second system embedding an IBM1 model

The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction

With the advent of deep learning, research in many areas of machine learning is converging towards the same set of methods and models. For example, long short-term memory networks are not only popular for various tasks in natural language processing (NLP) such as speech recognition, machine translation, handwriting recognition, syntactic parsing, etc., but they are also applicable to seemingly unrelated fields such as robot control, time series prediction, and bioinformatics. Recent advances in contextual word embeddings like BERT boast with achieving state-of-the-art results on 11 NLP tasks with the same model. Before deep learning, a speech recognizer and a syntactic parser used to have little in common as systems were much more tailored towards the task at hand. At the core of this development is the tendency to view each task as yet another data mapping problem, neglecting the particular characteristics and (soft) requirements tasks often have in practice. This often goes along with a sharp break of deep learning methods with previous research in the specific area. This work can be understood as an antithesis to this paradigm. We show how traditional symbolic statistical machine translation models can still improve neural machine translation (NMT) while reducing the risk for common pathologies of NMT such as hallucinations and neologisms. Other external symbolic models such as spell checkers and morphology databases help neural grammatical error correction. We also focus on language models that often do not play a role in vanilla end-to-end approaches and apply them in different ways to word reordering, grammatical error correction, low-resource NMT, and document-level NMT. Finally, we demonstrate the benefit of hierarchical models in sequence-to-sequence prediction. Hand-engineered covering grammars are effective in preventing catastrophic errors in neural text normalization systems. Our operation sequence model for interpretable NMT represents translation as a series of actions that modify the translation state, and can also be seen as derivation in a formal grammar.EPSRC grant EP/L027623/1 EPSRC Tier-2 capital grant EP/P020259/