Cross-lingual transfer parser from Hindi to Bengali using delexicalization and chunking

While statistical methods have been very effective in developing NLP tools, the use of linguistic tools and understanding of language structure can make these tools better. Cross-lingual parser construction has been used to develop parsers for languages with no annotated treebank. Delexicalized parsers that use only POS tags can be transferred to a new target language. But the success of a delexicalized transfer parser depends on the syntactic closeness between the source and target languages. The understanding of the linguistic similarities and differences between the languages can be used to improve the parser. In this paper, we use a method based on cross-lingual model transfer to transfer a Hindi parser to Bengali. The technique does not need any parallel corpora but makes use of chunkers of these languages. We observe that while the two languages share broad similarities, Bengali and Hindi phrases do not have identical construction. We can improve the transfer based parser if the parser is transferred at the chunk level. Based on this we present a method to use chunkers to develop a cross-lingual parser for Bengali which results in an improvement of unlabelled attachment score (UAS) from 65.1 (baseline parser) to 78.2.