Semantic Parsing on Freebase from Question-Answer Pairs

  • Berant J
  • Chou A
  • Frostig R
 et al. 
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In this paper, we train a semantic parser that scales up to Freebase. Instead of relying on annotated logical forms, which is especially expensive to obtain at large scale, we learn from question-answer pairs. The main challenge in this setting is narrowing down the huge number of possible logical predicates for a given question. We tackle this problem in two ways: First, we build a coarse mapping from phrases to predicates using a knowledge base and a large text corpus. Second, we use a bridging operation to generate additional predicates based on neighboring predicates. On the dataset of Cai andYates (2013), despite not having annotated logical forms, our system outperforms their state-of-the-art parser. Additionally, we collected a more realistic and challenging dataset of question-answer pairs and improves over a natural baseline.

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  • PMID: 2216100
  • PUI: 603563668
  • SGR: 84904308637
  • SCOPUS: 2-s2.0-84904308637
  • ISBN: 9781937284978


  • Jonathan Berant

  • Andrew Chou

  • Roy Frostig

  • Percy Liang

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