Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing

Chunyang Xiao, Christoph Teichmann, Konstantine Arkoudas


Abstract
While sequence-to-sequence (seq2seq) models achieve state-of-the-art performance in many natural language processing tasks, they can be too slow for real-time applications. One performance bottleneck is predicting the most likely next token over a large vocabulary; methods to circumvent this bottleneck are a current research topic. We focus specifically on using seq2seq models for semantic parsing, where we observe that grammars often exist which specify valid formal representations of utterance semantics. By developing a generic approach for restricting the predictions of a seq2seq model to grammatically permissible continuations, we arrive at a widely applicable technique for speeding up semantic parsing. The technique leads to a 74% speed-up on an in-house dataset with a large vocabulary, compared to the same neural model without grammatical restrictions
Anthology ID:
W19-3902
Volume:
Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges
Month:
August
Year:
2019
Address:
Florence
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14–23
Language:
URL:
https://aclanthology.org/W19-3902
DOI:
10.18653/v1/W19-3902
Bibkey:
Cite (ACL):
Chunyang Xiao, Christoph Teichmann, and Konstantine Arkoudas. 2019. Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing. In Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges, pages 14–23, Florence. Association for Computational Linguistics.
Cite (Informal):
Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing (Xiao et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-3902.pdf