Iterative Utterance Segmentation for Neural Semantic Parsing

No. 14: AAAI-21 Technical Tracks 14

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 35

AAAI Technical Track on Speech and Natural Language Processing I

Neural semantic parsers usually fail to parse long and complex utterances into correct meaning representations, due to the lack of exploiting the principle of compositionality. To address this issue, we present a novel framework for boosting neural semantic parsers via iterative utterance segmentation. Given an input utterance, our framework iterates between two neural modules: a segmenter for segmenting a span from the utterance, and a parser for mapping the span into a partial meaning representation. Then, these intermediate parsing results are composed into the final meaning representation. One key advantage is that this framework does not require any handcraft templates or additional labeled data for utterance segmentation: we achieve this through proposing a novel training method, in which the parser provides pseudo supervision for the segmenter. Experiments on Geo, ComplexWebQuestions and Formulas show that our framework can consistently improve performances of neural semantic parsers in different domains. On data splits that require compositional generalization, our framework brings significant accuracy gains: Geo 63.1~81.2, Formulas 59.7~72.7, ComplexWebQuestions 27.1~56.3.

10.1609/aaai.v35i14.17530