Character-level and syntax-level models for low-resource and multilingual natural language processing

There are more than 7000 languages in the world, but only a small portion of them benefit from Natural Language Processing resources and models. Although languages generally present different characteristics, “cross-lingual bridges” can be exploited, such as transliteration signals and word alignment links. Such information, together with the availability of multiparallel corpora and the urge to overcome language barriers, motivates us to build models that represent more of the world’s languages. This thesis investigates cross-lingual links for improving the processing of low-resource languages with language-agnostic models at the character and syntax level. Specifically, we propose to (i) use orthographic similarities and transliteration between Named Entities and rare words in different languages to improve the construction of Bilingual Word Embeddings (BWEs) and named entity resources, and (ii) exploit multiparallel corpora for projecting labels from high- to low-resource languages, thereby gaining access to weakly supervised processing methods for the latter. In the first publication, we describe our approach for improving the translation of rare words and named entities for the Bilingual Dictionary Induction (BDI) task, using orthography and transliteration information. In our second work, we tackle BDI by enriching BWEs with orthography embeddings and a number of other features, using our classification-based system to overcome script differences among languages. The third publication describes cheap cross-lingual signals that should be considered when building mapping approaches for BWEs since they are simple to extract, effective for bootstrapping the mapping of BWEs, and overcome the failure of unsupervised methods. The fourth paper shows our approach for extracting a named entity resource for 1340 languages, including very low-resource languages from all major areas of linguistic diversity. We exploit parallel corpus statistics and transliteration models and obtain improved performance over prior work. Lastly, the fifth work models annotation projection as a graph-based label propagation problem for the part of speech tagging task. Part of speech models trained on our labeled sets outperform prior work for low-resource languages like Bambara (an African language spoken in Mali), Erzya (a Uralic language spoken in Russia’s Republic of Mordovia), Manx (the Celtic language of the Isle of Man), and Yoruba (a Niger-Congo language spoken in Nigeria and surrounding countries)

A survey of cross-lingual word embedding models

Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing models for low-resource languages. In this survey, we provide a comprehensive typology of cross-lingual word embedding models. We compare their data requirements and objective functions. The recurring theme of the survey is that many of the models presented in the literature optimize for the same objectives, and that seemingly different models are often equivalent, modulo optimization strategies, hyper-parameters, and such. We also discuss the different ways cross-lingual word embeddings are evaluated, as well as future challenges and research horizons.</jats:p

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