Work

Macro Grammars and Holistic Triggering for Efficient Semantic Parsing

Year: 2017

Type: conference-paper

Abstract: To learn a semantic parser from denotations, a learning algorithm must search over a combinatorially large space of logical forms for ones consistent with the annotated denotations.We propose a new online learning algorithm that searches faster as training progresses.The two key ideas are using macro grammars to cache the abstract patterns of useful logical forms found thus far, and holistic triggering to efficiently retrieve the most relevant patterns based on sentence similarity.On the WIKITABLEQUESTIONS dataset, we first expand the search space of an existing model to improve the state-of-theart accuracy from 38.7% to 42.7%, and then use macro grammars and holistic triggering to achieve an 11x speedup and an accuracy of 43.7%.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d17-1125

Authors: Yuchen Zhang, Panupong Pasupat, Percy Liang

Institution: Stanford University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Tencent

OpenAlex ID: W2963319870