# Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing

arXiv: https://arxiv.org/abs/1906.07108 · HTML: https://mlsanity.com/paper/1906.07108/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

## Built on by (most cited first, 18 of 18)

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- [mathbbUSCD: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding](https://mlsanity.com/paper/2409.05923/) (2024)
- [JaCoText: A Pretrained Model for Java Code-Text Generation](https://mlsanity.com/paper/2303.12869/) (2023)
- [History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling](https://mlsanity.com/paper/2306.06872/) (2023)
- [Mirror: A Natural Language Interface for Data Querying, Summarization, and Visualization](https://mlsanity.com/paper/2303.08697/) (2023)
- [The Role of Global and Local Context in Named Entity Recognition](https://mlsanity.com/paper/2305.03132/) (2023)
- [SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization](https://mlsanity.com/paper/2303.14011/) (2023)

## Abstract (arXiv)

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.