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    <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.</abstract>
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%0 Conference Proceedings
%T Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing
%A Guo, Daya
%A Tang, Duyu
%A Duan, Nan
%A Zhou, Ming
%A Yin, Jian
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F guo-etal-2019-coupling
%X 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.
%R 10.18653/v1/P19-1082
%U https://aclanthology.org/P19-1082
%U https://doi.org/10.18653/v1/P19-1082
%P 855-866

Markdown (Informal)

[Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing](https://aclanthology.org/P19-1082) (Guo et al., ACL 2019)

• Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing (Guo et al., ACL 2019)

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.