Combining, Adapting and Reusing Bi-texts between Related Languages: Application to Statistical Machine Translation (invited talk)

摘要

Bilingual sentence-aligned parallel corpora, or bi-texts, are a useful resource for solving many computational linguistics problems including part-of-speech tagging, syntactic parsing, named entity recognition, word sense disambiguation, sentiment analysis, etc.; they are also a critical resource for some real-world applications such as statistical machine translation (SMT) and cross-language information retrieval. Unfortunately, building large bi-texts is hard, and thus most of the 6,500+ world languages remain resource-poor in bi-texts. However, many resource-poor languages are related to some resource-rich language, with whom they overlap in vocabulary and share cognates, which offers opportunities for using their bi-texts. We explore various options for bi-text reuse: (ⅰ) direct combination of bi-texts, (ⅱ) combination of models trained on such bi-texts, and (ⅲ) a sophisticated combination of (ⅰ) and (ⅱ). We further explore the idea of generating bi-texts for a resource-poor language by adapting a bi-text for a resource-rich language. We build a lattice of adaptation options for each word and phrase, and we then decode it using a language model for the resource-poor language. We compare word- and phrase-level adaptation, and we further make use of cross-language morphology. For the adaptation, we experiment with (a) a standard phrase-based SMT decoder, and (b) a specialized beam-search adaptation decoder. Finally, we observe that for closely-related languages, many of the differences are at the sub-word level. Thus, we explore the idea of reducing translation to character-level transliteration. We further demonstrate the potential of combining word- and character-level models.