Learning Translations for Tagged Words: Extending the Translation Lexicon of an ITG for Low Resource Languages

We tackle the challenge of learning part-ofspeech classified translations as part of an inversion transduction grammar, by learning translations for English words with known part-of-speech tags, both from existing translation lexica and from parallel corpora. When translating from a low resource language into English, we can expect to have rich resources for English, such as treebanks, and small amounts of bilingual resources, such as translation lexica and parallel corpora. We solve the problem of integrating these heterogeneous resources into a single model using stochastic Inversion Transduction Grammars, which we augment with wildcards to handle unknown translations.