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Shift-Reduce CCG Parsing with a Dependency Model

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We present the first dependency model for a shift-reduce CCG parser. Modelling dependencies is desirable for a number of reasons, including handling the “spurious” ambiguity of CCG ; fitting well with the theory of CCG ; and optimizing for structures which are evaluated at test time. We develop a novel training technique using a dependency oracle, in which all derivations are hidden. A challenge arises from the fact that the oracle needs to keep track of exponentially many gold-standard derivations, which is solved by integrating a packed parse forest with the beam-search decoder. Standard CCG Bank tests show the model achieves up to 1.05 labeled F-score improvements over three existing, competitive CCG parsing models.

This talk is part of the NLIP Seminar Series series.

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