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Papers

Constructive Type-Logical Supertagging with Self-Attention Networks

2019-05-31 · WS 2019 8 · Konstantinos Kogkalidis, Michael Moortgat, Tejaswini Deoskar

We propose a novel application of self-attention networks towards grammar induction. We present an attention-based supertagger for a refined type-logical grammar, trained on constructing types inductively. In addition to achieving a high overall type accuracy, our model is able to learn the syntax of the grammar's type system along with its denotational semantics. This lifts the closed world assumption commonly made by lexicalized grammar supertaggers, greatly enhancing its generalization potential. This is evidenced both by its adequate accuracy over sparse word types and its ability to correctly construct complex types never seen during training, which, to the best of our knowledge, was as of yet unaccomplished.

📄 PDF Abstract BibTeX arXiv:1905.13418

Code (1)

konstantinosKokos/Lassy-TLG-Supertagging 공식 구현 pytorch

Tasks

Vocal Bursts Type Prediction

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