Grasp: Randomised Semiring Parsing

We present a suite of algorithms for inference tasks over (finite and infinite) context-free sets. For generality and clarity, we have chosen the framework of semiring parsing with support to the most common semirings (e.g. Forest, Viterbi, k-best and Inside). We see parsing from the more general viewpoint of weighted deduction allowing for arbitrary weighted finite-state input and provide implementations of both bottom-up (CKY-inspired) and top-down (Earley-inspired) algorithms. We focus on approximate inference by Monte Carlo methods and provide implementations of ancestral sampling and slice sampling. In principle, sampling methods can deal with models whose independence assumptions are weaker than what is feasible by standard dynamic programming. We envision applications such as monolingual constituency parsing, synchronous parsing, context-free models of reordering for machine translation, and machine translation decoding

cdec: A Decoder, Alignment, and Learning Framework for Finite-State and Context-Free Translation Models

We present cdec, an open source framework for decoding, aligning with, and training a number of statistical machine translation models, including word-based models, phrase-based models, and models based on synchronous context-free grammars. Using a single unified internal representation for translation forests, the decoder strictly separates model-specific translation logic from general rescoring, pruning, and inference algorithms. From this unified representation, the decoder can extract not only the 1- or k-best translations, but also alignments to a reference, or the quantities necessary to drive discriminative training using gradient-based or gradient-free optimization techniques. Its efficient C++ implementation means that memory use and runtime performance are significantly better than comparable decoders.

Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation

The quality of statistical machine translation performed with phrase based approaches can be increased by permuting the words in the source sentences in an order which resembles that of the target language. We propose a class of recurrent neural models which exploit source side dependency syntax features to reorder the words into a target-like order. We evaluate these models on the German-to-English language pair, showing significant improvements over a phrase-based Moses baseline, obtaining a quality similar or superior to that of hand-coded syntactical reordering rules

A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena

Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs. Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials. To orientate the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon. The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling. We then question why some approaches are more successful than others in different language pairs. We argue that, besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair. To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge. Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic

A Comparison of Loopy Belief Propagation and Dual Decomposition for Integrated CCG Supertagging and Parsing

Via an oracle experiment, we show that the upper bound on accuracy of a CCG parser is significantly lowered when its search space is pruned using a supertagger, though the supertagger also prunes many bad parses. Inspired by this analysis, we design a single model with both supertagging and parsing features, rather than separating them into distinct models chained together in a pipeline. To overcome the resulting increase in complexity, we experiment with both belief propagation and dual decomposition approaches to inference, the first empirical comparison of these algorithms that we are aware of on a structured natural language processing problem. On CCGbank we achieve a labelled dependency F-measure of 88.8 % on gold POS tags, and 86.7 % on automatic part-of-speeoch tags, the best reported results for this task.

Exact Decoding for Phrase-Based Statistical Machine Translation

Abstract The combinatorial space of translation derivations in phrase-based statistical machine translation is given by the intersection between a translation lattice and a target language model. We replace this intractable intersection by a tractable relaxation which incorporates a low-order upperbound on the language model. Exact optimisation is achieved through a coarseto-fine strategy with connections to adaptive rejection sampling. We perform exact optimisation with unpruned language models of order 3 to 5 and show searcherror curves for beam search and cube pruning on standard test sets. This is the first work to tractably tackle exact optimisation with language models of orders higher than 3

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

A Formal Model of Ambiguity and its Applications in Machine Translation

Systems that process natural language must cope with and resolve ambiguity. In this dissertation, a model of language processing is advocated in which multiple inputs and multiple analyses of inputs are considered concurrently and a single analysis is only a last resort. Compared to conventional models, this approach can be understood as replacing single-element inputs and outputs with weighted sets of inputs and outputs. Although processing components must deal with sets (rather than individual elements), constraints are imposed on the elements of these sets, and the representations from existing models may be reused. However, to deal efficiently with large (or infinite) sets, compact representations of sets that share structure between elements, such as weighted finite-state transducers and synchronous context-free grammars, are necessary. These representations and algorithms for manipulating them are discussed in depth in depth. To establish the effectiveness and tractability of the proposed processing model, it is applied to several problems in machine translation. Starting with spoken language translation, it is shown that translating a set of transcription hypotheses yields better translations compared to a baseline in which a single (1-best) transcription hypothesis is selected and then translated, independent of the translation model formalism used. More subtle forms of ambiguity that arise even in text-only translation (such as decisions conventionally made during system development about how to preprocess text) are then discussed, and it is shown that the ambiguity-preserving paradigm can be employed in these cases as well, again leading to improved translation quality. A model for supervised learning that learns from training data where sets (rather than single elements) of correct labels are provided for each training instance and use it to learn a model of compound word segmentation is also introduced, which is used as a preprocessing step in machine translation