# Robust Semantic Parsing with Adversarial Learning for Domain Generalization

arXiv: https://arxiv.org/abs/1910.06700 · HTML: https://mlsanity.com/paper/1910.06700/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [Measure and Improve Robustness in NLP Models: A Survey](https://mlsanity.com/paper/2112.08313/) (2021)
- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis](https://mlsanity.com/paper/2402.14536/) (2024)
- [Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge](https://mlsanity.com/paper/2207.04913/) (2022)
- [A linguistically-motivated evaluation methodology for unraveling model's abilities in reading comprehension tasks](https://mlsanity.com/paper/2501.17569/) (2025)
- [Lexicalized Constituency Parsing for Middle Dutch: Low-resource Training and Cross-Domain Generalization](https://mlsanity.com/paper/2601.07008/) (2026)

## Abstract (arXiv)

This paper addresses the issue of generalization for Semantic Parsing in an adversarial framework. Building models that are more robust to inter-document variability is crucial for the integration of Semantic Parsing technologies in real applications. The underlying question throughout this study is whether adversarial learning can be used to train models on a higher level of abstraction in order to increase their robustness to lexical and stylistic variations.We propose to perform Semantic Parsing with a domain classification adversarial task without explicit knowledge of the domain. The strategy is first evaluated on a French corpus of encyclopedic documents, annotated with FrameNet, in an information retrieval perspective, then on PropBank Semantic Role Labeling task on the CoNLL-2005 benchmark. We show that adversarial learning increases all models generalization capabilities both on in and out-of-domain data.