Year: 2021
Type: conference-paper
Abstract: In interpretable NLP, we require faithful rationales that reflect the model's decision-making process for an explained instance.While prior work focuses on extractive rationales (a subset of the input words), we investigate their lessstudied counterpart: free-text natural language rationales.We demonstrate that pipelines, models for faithful rationalization on informationextraction style tasks, do not work as well on "reasoning" tasks requiring free-text rationales.We turn to models that jointly predict and rationalize, a class of widely used high-performance models for free-text rationalization.We investigate the extent to which the labels and rationales predicted by these models are associated, a necessary property of faithful explanation.Via two tests, robustness equivalence and feature importance agreement, we find that stateof-the-art T5-based joint models exhibit desirable properties for explaining commonsense question-answering and natural language inference, indicating their potential for producing faithful free-text rationales. 1
Authors: Sarah Wiegreffe, Ana Marasović, Noah A. Smith
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3093530468