In current systems for syntactic and semantic dependency parsing, people usually define a very high-dimensional feature space to achieve good performance. But these systems often suffer severe performance drops on outof-domain test data due to the diversity of features of different domains. This paper focuses on how to relieve this domain adaptation problem with the help of unlabeled target domain data. We propose a deep learning method to adapt both syntactic and semantic parsers. With additional unlabeled target domain data, our method can learn a latent feature representation (LFR) that is beneficial to both domains. Experiments on English data in the CoNLL 2009 shared task show that our method largely reduced the performance drop on out-of-domain test data. Moreover, we get a Macro F1 score that is 2.32 points higher than the best system in the CoNLL 2009 shared task in out-of-domain tests.
REF report the smallest performance difference (5.5 points in F 1 ) between in-domain and out-of-domain test data, leading to the best results to date on the CoNLL 2009 out-of-domain test.
Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks
Transactions of the Association for Computational Linguistics
Transactions of the Association for Computational Linguistics
Computer Science