DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation
Published in ACL, 2022
Recommended citation: Nitay Calderon*, Eyal Ben-David*, Amir Feder and Roi Reichart
*Equal contribution https://doi.org/10.18653/v1/2022.acl-long.533
Abstract
Natural language processing (NLP) algorithms have become very successful, but they still struggle when applied to out-of-distribution examples. In this paper we propose a controllable generation approach in order to deal with this domain adaptation (DA) challenge. Given an input text example, our DoCoGen algorithm generates a domain-counterfactual textual example (D-con) - that is similar to the original in all aspects, including the task label, but its domain is changed to a desired one. Importantly, DoCoGen is trained using only unlabeled examples from multiple domains - no NLP task labels or parallel pairs of textual examples and their domain-counterfactuals are required. We show that DoCoGen can generate coherent counterfactuals consisting of multiple sentences. We use the D-cons generated by DoCoGen to augment a sentiment classifier and a multi-label intent classifier in 20 and 78 DA setups, respectively, where source-domain labeled data is scarce. Our model outperforms strong baselines and improves the accuracy of a state-of-the-art unsupervised DA algorithm.bibtex
@inproceedings{calderon2022docogen,
author = {Nitay Calderon and
Eyal Ben{-}David and
Amir Feder and
Roi Reichart},
editor = {Smaranda Muresan and
Preslav Nakov and
Aline Villavicencio},
title = {DoCoGen: Domain Counterfactual Generation for Low Resource Domain
Adaptation},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational
Linguistics (Volume 1: Long Papers), {ACL} 2022, Dublin, Ireland,
May 22-27, 2022},
pages = {7727--7746},
publisher = {Association for Computational Linguistics},
year = {2022},
url = {https://doi.org/10.18653/v1/2022.acl-long.533},
doi = {10.18653/v1/2022.acl-long.533},
timestamp = {Mon, 01 Aug 2022 16:27:51 +0200},
biburl = {https://dblp.org/rec/conf/acl/CalderonBFR22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}