Abstract
Weakly-supervised semantic parsers are trained on utterance-denotation pairs, treating logical forms as latent. The task is challenging due to the large search space and spuriousness of logical forms. In this paper we introduce a neural parser-ranker system for weakly-supervised semantic parsing. The parser generates candidate tree-structured logical forms from utterances using clues of denotations. These candidates are then ranked based on two criterion: their likelihood of executing to the correct denotation, and their agreement with the utterance semantics. We present a scheduled training procedure to balance the contribution of the two objectives. Furthermore, we propose to use a neurally encoded lexicon to inject prior domain knowledge to the model. Experiments on three Freebase datasets demonstrate the effectiveness of our semantic parser, achieving results within the state-of-the-art range.- Anthology ID:
- K18-1035
- Volume:
- Proceedings of the 22nd Conference on Computational Natural Language Learning
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Anna Korhonen, Ivan Titov
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 356–367
- Language:
- URL:
- https://aclanthology.org/K18-1035
- DOI:
- 10.18653/v1/K18-1035
- Cite (ACL):
- Jianpeng Cheng and Mirella Lapata. 2018. Weakly-Supervised Neural Semantic Parsing with a Generative Ranker. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 356–367, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Weakly-Supervised Neural Semantic Parsing with a Generative Ranker (Cheng & Lapata, CoNLL 2018)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/K18-1035.pdf