Soft syntactic constraints for word alignment through discriminative training

Word alignment methods can gain valuable guidance by ensuring that their alignments maintain cohesion with respect to the phrases specified by a monolingual dependency tree. However, this hard constraint can also rule out correct alignments, and its utility decreases as alignment models become more complex. We use a publicly available structured output SVM to create a max-margin syntactic aligner with a soft cohesion constraint. The resulting aligner is the first, to our knowledge, to use a discriminative learning method to train an ITG bitext parser.

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

Conditional Random Field Autoencoders for Unsupervised Structured Prediction

We introduce a framework for unsupervised learning of structured predictors with overlapping, global features. Each input's latent representation is predicted conditional on the observable data using a feature-rich conditional random field. Then a reconstruction of the input is (re)generated, conditional on the latent structure, using models for which maximum likelihood estimation has a closed-form. Our autoencoder formulation enables efficient learning without making unrealistic independence assumptions or restricting the kinds of features that can be used. We illustrate insightful connections to traditional autoencoders, posterior regularization and multi-view learning. We show competitive results with instantiations of the model for two canonical NLP tasks: part-of-speech induction and bitext word alignment, and show that training our model can be substantially more efficient than comparable feature-rich baselines

Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure

It has been established that incorporating word cluster features derived from large unlabeled corpora can significantly improve prediction of linguistic structure. While previous work has focused primarily on English, we extend these results to other languages along two dimensions. First, we show that these results hold true for a number of languages across families. Second, and more interestingly, we provide an algorithm for inducing cross-lingual clusters and we show that features derived from these clusters significantly improve the accuracy of cross-lingual structure prediction. Specifically, we show that by augmenting direct-transfer systems with cross-lingual cluster features, the relative error of delexicalized dependency parsers, trained on English treebanks and transferred to foreign languages, can be reduced by up to 13%. When applying the same method to direct transfer of named-entity recognizers, we observe relative improvements of up to 26%

HMM word-to-phrase alignment with dependency constraints

In this paper, we extend the HMMwordto-phrase alignment model with syntactic dependency constraints. The syntactic dependencies between multiple words in one language are introduced into the model in a bid to produce coherent alignments. Our experimental results on a variety of Chinese–English data show that our syntactically constrained model can lead to as much as a 3.24% relative improvement in BLEU score over current HMM word-to-phrase alignment models on a Phrase-Based Statistical Machine Translation system when the training data is small, and a comparable performance compared to IBM model 4 on a Hiero-style system with larger training data. An intrinsic alignment quality evaluation shows that our alignment model with dependency constraints leads to improvements in both precision (by 1.74% relative) and recall (by 1.75% relative) over the model without dependency information

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

Constrained word alignment models for statistical machine translation

Word alignment is a fundamental and crucial component in Statistical Machine Translation (SMT) systems. Despite the enormous progress made in the past two decades, this task remains an active research topic simply because the quality of word alignment is still far from optimal. Most state-of-the-art word alignment models are grounded on statistical learning theory treating word alignment as a general sequence alignment problem, where many linguistically motivated insights are not incorporated. In this thesis, we propose new word alignment models with linguistically motivated constraints in a bid to improve the quality of word alignment for Phrase-Based SMT systems (PB-SMT). We start the exploration with an investigation into segmentation constraints for word alignment by proposing a novel algorithm, namely word packing, which is motivated by the fact that one concept expressed by one word in one language can frequently surface as a compound or collocation in another language. Our algorithm takes advantage of the interaction between segmentation and alignment, starting with some segmentation for both the source and target language and updating the segmentation with respect to the word alignment results using state-of-the-art word alignment models; thereafter a refined word alignment can be obtained based on the updated segmentation. In this process, the updated segmentation acts as a hard constraint on the word alignment models and reduces the complexity of the alignment models by generating more 1-to-1 correspondences through word packing. Experimental results show that this algorithm can lead to statistically significant improvements over the state-of-the-art word alignment models. Given that word packing imposes "hard" segmentation constraints on the word aligner, which is prone to introducing noise, we propose two new word alignment models using syntactic dependencies as soft constraints. The first model is a syntactically enhanced discriminative word alignment model, where we use a set of feature functions to express the syntactic dependency information encoded in both source and target languages. One the one hand, this model enjoys great flexibility in its capacity to incorporate multiple features; on the other hand, this model is designed to facilitate model tuning for different objective functions. Experimental results show that using syntactic constraints can improve the performance of the discriminative word alignment model, which also leads to better PB-SMT performance compared to using state-of-the-art word alignment models. The second model is a syntactically constrained generative word alignment model, where we add in a syntactic coherence model over the target phrases in the context of HMM word-to-phrase alignment. The advantages of our model are that (i) the addition of the syntactic coherence model preserves the efficient parameter estimation procedures; and (ii) the flexibility of the model can be increased so that it can be tuned according to different objective functions. Experimental results show that tuning this model properly leads to a significant gain in MT performance over the state-of-the-art

Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision

Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. As an alternative, this dissertation considers methods for linguistic structure prediction that can make use of incomplete and cross-lingual supervision, with the prospect of making linguistic processing tools more widely available at a lower cost. An overarching theme of this work is the use of structured discriminative latent variable models for learning with indirect and ambiguous supervision; as instantiated, these models admit rich model features while retaining efficient learning and inference properties. The first contribution to this end is a latent-variable model for fine-grained sentiment analysis with coarse-grained indirect supervision. The second is a model for cross-lingual word-cluster induction and the application thereof to cross-lingual model transfer. The third is a method for adapting multi-source discriminative cross-lingual transfer models to target languages, by means of typologically informed selective parameter sharing. The fourth is an ambiguity-aware self- and ensemble-training algorithm, which is applied to target language adaptation and relexicalization of delexicalized cross-lingual transfer parsers. The fifth is a set of sequence-labeling models that combine constraints at the level of tokens and types, and an instantiation of these models for part-of-speech tagging with incomplete cross-lingual and crowdsourced supervision. In addition to these contributions, comprehensive overviews are provided of structured prediction with no or incomplete supervision, as well as of learning in the multilingual and cross-lingual settings. Through careful empirical evaluation, it is established that the proposed methods can be used to create substantially more accurate tools for linguistic processing, compared to both unsupervised methods and to recently proposed cross-lingual methods. The empirical support for this claim is particularly strong in the latter case; our models for syntactic dependency parsing and part-of-speech tagging achieve the hitherto best published results for a wide number of target languages, in the setting where no annotated training data is available in the target language

Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing

Linguistic typology aims to capture structural and semantic variation across the world's languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-employment of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such approach could be facilitated by recent developments in data-driven induction of typological knowledge