Beyond Word N-Grams

We describe, analyze, and evaluate experimentally a new probabilistic model for word-sequence prediction in natural language based on prediction suffix trees (PSTs). By using efficient data structures, we extend the notion of PST to unbounded vocabularies. We also show how to use a Bayesian approach based on recursive priors over all possible PSTs to efficiently maintain tree mixtures. These mixtures have provably and practically better performance than almost any single model. We evaluate the model on several corpora. The low perplexity achieved by relatively small PST mixture models suggests that they may be an advantageous alternative, both theoretically and practically, to the widely used n-gram models.Comment: 15 pages, one PostScript figure, uses psfig.sty and fullname.sty. Revised version of a paper in the Proceedings of the Third Workshop on Very Large Corpora, MIT, 199

Exact decoding for phrase-based statistical machine translation

© 2014 Association for Computational Linguistics. 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

Decision Tree-based Syntactic Language Modeling

Statistical Language Modeling is an integral part of many natural language processing applications, such as Automatic Speech Recognition (ASR) and Machine Translation. N-gram language models dominate the field, despite having an extremely shallow view of language---a Markov chain of words. In this thesis, we develop and evaluate a joint language model that incorporates syntactic and lexical information in a effort to ``put language back into language modeling.'' Our main goal is to demonstrate that such a model is not only effective but can be made scalable and tractable. We utilize decision trees to tackle the problem of sparse parameter estimation which is exacerbated by the use of syntactic information jointly with word context. While decision trees have been previously applied to language modeling, there has been little analysis of factors affecting decision tree induction and probability estimation for language modeling. In this thesis, we analyze several aspects that affect decision tree-based language modeling, with an emphasis on syntactic language modeling. We then propose improvements to the decision tree induction algorithm based on our analysis, as well as the methods for constructing forest models---models consisting of multiple decision trees. Finally, we evaluate the impact of our syntactic language model on large scale Speech Recognition and Machine Translation tasks. In this thesis, we also address a number of engineering problems associated with the joint syntactic language model in order to make it tractable. Particularly, we propose a novel decoding algorithm that exploits the decision tree structure to eliminate unnecessary computation. We also propose and evaluate an approximation of our syntactic model by word n-grams---the approximation that makes it possible to incorporate our model directly into the CDEC Machine Translation decoder rather than using the model for rescoring hypotheses produced using an n-gram model