Approaching new data can be quite deterrent; you do not know how your categories of interest are realized in it, commonly, there is no labeled data at hand, and the performance of domain adaptation methods is unsatisfactory. Aiming to assist domain experts in their first steps into a new task over a new corpus, we present an unsupervised approach to reveal complex rules which cluster the unexplored corpus by its prominent categories (or facets). These rules are human-readable, thus providing an important ingredient which has become in short supply lately - explainability. Each rule provides an explanation for the commonality of all the texts it clusters together. The experts can then identify which rules best capture texts of their categories of interest, and utilize them to deepen their understanding of these categories. These rules can also bootstrap the process of data labeling by pointing at a subset of the corpus which is enriched with texts demonstrating the target categories. We present an extensive evaluation of the usefulness of these rules in identifying target categories, as well as a user study which assesses their interpretability.
@inproceedings{DBLP:conf/emnlp/ShnarchCMAS20,
author = {Eyal Shnarch and
Leshem Choshen and
Guy Moshkowich and
Ranit Aharonov and
Noam Slonim},
editor = {Trevor Cohn and
Yulan He and
Yang Liu},
title = {Unsupervised Expressive Rules Provide Explainability and Assist Human
Experts Grasping New Domains},
booktitle = {Findings of the Association for Computational Linguistics: {EMNLP}
2020, Online Event, 16-20 November 2020},
series = {Findings of {ACL}},
volume = {{EMNLP} 2020},
pages = {2678--2697},
publisher = {Association for Computational Linguistics},
year = {2020},
url = {https://doi.org/10.18653/v1/2020.findings-emnlp.243},
doi = {10.18653/V1/2020.FINDINGS-EMNLP.243},
timestamp = {Tue, 20 Aug 2024 07:54:42 +0200},
biburl = {https://dblp.org/rec/conf/emnlp/ShnarchCMAS20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
See the full reference list in the paper.