Using Comparable Corpora to Adapt MT Models to New Domains

In previous work we showed that when using an SMT model trained on old-domain data to translate text in a new-domain, most errors are due to unseen source words, unseen target translations, and inaccurate translation model scores (Irvine et al., 2013a). In this work, we target errors due to inaccurate translation model scores using new-domain comparable corpora, which we mine from Wikipedia. We assume that we have access to a large olddomain parallel training corpus but only enough new-domain parallel data to tune model parameters and do evaluation. We use the new-domain comparable corpora to estimate additional feature scores over the phrase pairs in our baseline models. Augmenting models with the new features improves the quality of machine translations in the medical and science domains by up to 1.3 BLEU points over very strong baselines trained on the 150 million word Canadian Hansard dataset.

Using Comparable Corpora to Augment Statistical Machine Translation Models in Low Resource Settings

Previously, statistical machine translation (SMT) models have been estimated from parallel corpora, or pairs of translated sentences. In this thesis, we directly incorporate comparable corpora into the estimation of end-to-end SMT models. In contrast to parallel corpora, comparable corpora are pairs of monolingual corpora that have some cross-lingual similarities, for example topic or publication date, but that do not necessarily contain any direct translations. Comparable corpora are more readily available in large quantities than parallel corpora, which require significant human effort to compile. We use comparable corpora to estimate machine translation model parameters and show that doing so improves performance in settings where a limited amount of parallel data is available for training. The major contributions of this thesis are the following: * We release ‘language packs’ for 151 human languages, which include bilingual dictionaries, comparable corpora of Wikipedia document pairs, comparable corpora of time-stamped news text that we harvested from the web, and, for non-roman script languages, dictionaries of name pairs, which are likely to be transliterations. * We present a novel technique for using a small number of example word translations to learn a supervised model for bilingual lexicon induction which takes advantage of a wide variety of signals of translation equivalence that can be estimated over comparable corpora. * We show that using comparable corpora to induce new translations and estimate new phrase table feature functions improves end-to-end statistical machine translation performance for low resource language pairs as well as domains. * We present a novel algorithm for composing multiword phrase translations from multiple unigram translations and then use comparable corpora to prune the large space of hypothesis translations. We show that these induced phrase translations improve machine translation performance beyond that of component unigrams. This thesis focuses on critical low resource machine translation settings, where insufficient parallel corpora exist for training statistical models. We experiment with both low resource language pairs and low resource domains of text. We present results from our novel error analysis methodology, which show that most translation errors in low resource settings are due to unseen source language words and phrases and unseen target language translations. We also find room for fixing errors due to how different translations are weighted, or scored, in the models. We target both error types; we use comparable corpora to induce new word and phrase translations and estimate novel translation feature scores. Our experiments show that augmenting baseline SMT systems with new translations and features estimated over comparable corpora improves translation performance significantly. Additionally, our techniques expand the applicability of statistical machine translation to those language pairs for which zero parallel text is available