# Practical Semantic Parsing for Spoken Language Understanding

arXiv: https://arxiv.org/abs/1903.04521 · HTML: https://mlsanity.com/paper/1903.04521/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2019.

## Built on by (most cited first, 7 of 7)

- [FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations](https://mlsanity.com/paper/2204.06508/) (2022)
- [Zero and Few-shot Semantic Parsing with Ambiguous Inputs](https://mlsanity.com/paper/2306.00824/) (2023)
- [Automatic Intent-Slot Induction for Dialogue Systems](https://mlsanity.com/paper/2103.08886/) (2021)
- [Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers](https://mlsanity.com/paper/2109.05186/) (2021)
- [Reducing Model Jitter: Stable Re-training of Semantic Parsers in Production Environments](https://mlsanity.com/paper/2204.04735/) (2022)
- [One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets](https://mlsanity.com/paper/2106.04476/) (2021)
- [Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation](https://mlsanity.com/paper/2109.03808/) (2021)

## Abstract (arXiv)

Executable semantic parsing is the task of converting natural language utterances into logical forms that can be directly used as queries to get a response. We build a transfer learning framework for executable semantic parsing. We show that the framework is effective for Question Answering (Q&A) as well as for Spoken Language Understanding (SLU). We further investigate the case where a parser on a new domain can be learned by exploiting data on other domains, either via multi-task learning between the target domain and an auxiliary domain or via pre-training on the auxiliary domain and fine-tuning on the target domain. With either flavor of transfer learning, we are able to improve performance on most domains; we experiment with public data sets such as Overnight and NLmaps as well as with commercial SLU data. The experiments carried out on data sets that are different in nature show how executable semantic parsing can unify different areas of NLP such as Q&A and SLU.