A Bayesian model for joint word alignment and part-of-speech transfer

COLING 2016 Robert {\"O}stling

Current methods for word alignment require considerable amounts of parallel text to deliver accurate results, a requirement which is met only for a small minority of the world{'}s approximately 7,000 languages. We show that by jointly performing word alignment and annotation transfer in a novel Bayesian model, alignment accuracy can be improved for language pairs where annotations are available for only one of the languages{---}a finding which could facilitate the study and processing of a vast number of low-resource languages... (read more)

PDF Abstract COLING 2016 PDF COLING 2016 Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet