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Semantic Parsing via Paraphrasing

Summary

A semantic parser maps a natural-language question to a logical form that can be executed against a knowledge base. The central problem is that the same knowledge-base predicate can be phrased in countless ways. Conventional parsers are trained only on text aligned with the knowledge base, so vast amounts of ordinary text go unused. Berant and Liang turn the task around. First, simple deterministic rules start from the entities in the question and construct a set of candidate logical forms. For each form, a "canonical" utterance is generated from the predicate descriptions in Freebase. A paraphrase model then selects the utterance closest in meaning to the input question and returns the corresponding logical form. Paraphrases are scored by two models: an association model built on the PARALEX corpus and a vector space model. They are trained jointly from question–answer pairs alone. The PARASEMPRE system improved on the then state-of-the-art result on WebQuestions and matched the best system on Free917. The work shows that text not aligned with the knowledge base can also help train semantic parsers.

Key points

  • Data: WebQuestions contains 5,810 question–answer pairs drawn from real web queries; Free917 contains 917 questions written by annotators. The knowledge base is Freebase: about 41M entities, 19K properties, and 596M assertions. Logical forms are expressed in simple λ-DCS.
  • Results: on WebQuestions, accuracy improved by about 12% relative to the previous state-of-the-art; on Free917, the result is on par with the best system. Simple baselines (Jaccard, Edit, WDDC06) perform reasonably well on Free917 because the vocabulary of its questions is close to Freebase phrasing, but they are considerably weaker on WebQuestions.
  • Model: a discriminative log-linear model combines paraphrase features with logical-form features. Training uses AdaGrad with L1 regularization and a beam of size 2,000. Candidate logical forms are built from 5 templates, including ones for multi-argument events and for aggregation (count).
  • Resources: the association model relies on the PARALEX monolingual parallel corpus, and the vector space model on word vectors trained on Wikipedia. The PARASEMPRE code has been released as part of SEMPRE (Stanford NLP).
  • Limitations: error analysis revealed spurious word matches in the paraphrase model, entity recognition errors, and a lack of support for temporal information. The approach is designed for factoid questions with limited compositionality.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Machine translation · AI & machine learning

Topics Knowledge graphs & ontologies Semantic parsing & meaning representation

 Institutions

🎓Stanford UniversityStanford · United States248 works · 214,804 citations

Cite

Cite as Jonathan Berant and Percy Liang. 2014. Semantic Parsing via Paraphrasing. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.3115/v1/p14-1133

@inproceedings{berant2014semantic,
  title = {{Semantic Parsing via Paraphrasing}},
  author = {Jonathan Berant and Percy Liang},
  booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2014},
  doi = {10.3115/v1/p14-1133},
  url = {https://doi.org/10.3115/v1/p14-1133}
}

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