# Learning to Map Context-Dependent Sentences to Executable Formal Queries

arXiv: https://arxiv.org/abs/1804.06868 · HTML: https://mlsanity.com/paper/1804.06868/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2018.

## Built on by (most cited first, 23 of 23)

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- [Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation](https://mlsanity.com/paper/2106.04447/) (2021)
- [Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2401.01711/) (2024)
- [Memory-Based Semantic Parsing](https://mlsanity.com/paper/2110.07358/) (2021)
- [History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling](https://mlsanity.com/paper/2306.06872/) (2023)
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- [lilGym: Natural Language Visual Reasoning with Reinforcement Learning](https://mlsanity.com/paper/2211.01994/) (2022)
- [Conversational Semantic Parsing using Dynamic Context Graphs](https://mlsanity.com/paper/2305.06164/) (2023)
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- [Few-Shot Adaptation for Parsing Contextual Utterances with LLMs](https://mlsanity.com/paper/2309.10168/) (2023)
- [Semantic Parsing in Limited Resource Conditions](https://mlsanity.com/paper/2309.07429/) (2023)

## Abstract (arXiv)

We propose a context-dependent model to map utterances within an interaction to executable formal queries. To incorporate interaction history, the model maintains an interaction-level encoder that updates after each turn, and can copy sub-sequences of previously predicted queries during generation. Our approach combines implicit and explicit modeling of references between utterances. We evaluate our model on the ATIS flight planning interactions, and demonstrate the benefits of modeling context and explicit references.