Self-training a Constituency Parser using n-gram Trees

Abstract

In this study, we tackle the problem of self-training a feature-rich discriminative constituency parser. We approach the self-training problem with the assumption that while the full sentence parse tree produced by a parser may contain errors, some portions of it are more likely to be correct. We hypothesize that instead of feeding the parser the guessed full sentence parse trees of its own, we can break them down into smaller ones, namely n-gram trees, and perform self-training on them. We build an n-gram parser and transfer the distinct expertise of the n-gram parser to the full sentence parser by using the Hierarchical Joint Learning (HJL) approach. The resulting jointly self-trained parser obtains slight improvement over the baseline.

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