We consider a very simple, yet effective, approach to cross language adaptation of dependency parsers. We first remove lexical items from the treebanks and map part-of-speech tags into a common tagset. We then train a language model on tag sequences in otherwise unlabeled target data and rank labeled source data by perplexity per word of tag sequences from less similar to most similar to the target. We then train our target language parser on the most similar data points in the source labeled data. The strategy achieves much better results than a non-adapted baseline and stateof-the-art unsupervised dependency parsing, and results are comparable to more complex projection-based cross language adaptation algorithms.
REF delexicalises the treebanks and maps the part-of-speech tags into one common tagset.
Data point selection for cross-language adaptation of dependency parsers
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Computer Science