Improving cross-lingual model transfer by chunking
This work addresses cross-lingual transfer challenges for NLP applications, but appears incremental as it builds on existing chunking methods.
The authors tackled the problem of syntactic differences hindering cross-lingual model transfer by using chunks or phrases as transfer units, resulting in improved effectiveness in addressing word and phrase ordering variations.
We present a shallow parser guided cross-lingual model transfer approach in order to address the syntactic differences between source and target languages more effectively. In this work, we assume the chunks or phrases in a sentence as transfer units in order to address the syntactic differences between the source and target languages arising due to the differences in ordering of words in the phrases and the ordering of phrases in a sentence separately.