Paper: Improved Parsing and POS Tagging Using Inter-Sentence Consistency Constraints

ACL ID D12-1131
Title Improved Parsing and POS Tagging Using Inter-Sentence Consistency Constraints
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

State-of-the-art statistical parsers and POS taggers perform very well when trained with large amounts of in-domain data. When train- ing data is out-of-domain or limited, accuracy degrades. In this paper, we aim to compen- sate for the lack of available training data by exploiting similarities between test set sen- tences. We show how to augment sentence- level models for parsing and POS tagging with inter-sentence consistency constraints. To deal with the resulting global objective, we present an efficient and exact dual decomposition de- coding algorithm. In experiments, we add consistency constraints to the MST parser and the Stanford part-of-speech tagger and demonstrate significant error reduction in the domain adaptation and the lightly supervised settings across five languages.