Abstract
In this paper, we present an approach to incorporate retrieved datapoints as supporting evidence for context-dependent semantic parsing, such as generating source code conditioned on the class environment. Our approach naturally combines a retrieval model and a meta-learner, where the former learns to find similar datapoints from the training data, and the latter considers retrieved datapoints as a pseudo task for fast adaptation. Specifically, our retriever is a context-aware encoder-decoder model with a latent variable which takes context environment into consideration, and our meta-learner learns to utilize retrieved datapoints in a model-agnostic meta-learning paradigm for fast adaptation. We conduct experiments on CONCODE and CSQA datasets, where the context refers to class environment in JAVA codes and conversational history, respectively. We use sequence-to-action model as the base semantic parser, which performs the state-of-the-art accuracy on both datasets. Results show that both the context-aware retriever and the meta-learning strategy improve accuracy, and our approach performs better than retrieve-and-edit baselines.- Anthology ID:
- P19-1082
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 855–866
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/P19-1082/
- DOI:
- 10.18653/v1/P19-1082
- Cite (ACL):
- Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, and Jian Yin. 2019. Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 855–866, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing (Guo et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/P19-1082.pdf
- Data
- CONCODE, CSQA