Plausible Extractive Rationalization through Semi-Supervised Entailment Signal

TL;DR

This paper introduces a semi-supervised method using entailment signals from a pre-trained NLI model to improve extractive rationalization, achieving high plausibility and performance without extensive labeled data.

Contribution

It proposes a semi-supervised entailment alignment approach for extractive rationalization, reducing the need for large annotated datasets.

Findings

Achieves comparable results to fully supervised models.

Outperforms unsupervised methods by over 100%.

Effective in question-answering tasks.

Abstract

The increasing use of complex and opaque black box models requires the adoption of interpretable measures, one such option is extractive rationalizing models, which serve as a more interpretable alternative. These models, also known as Explain-Then-Predict models, employ an explainer model to extract rationales and subsequently condition the predictor with the extracted information. Their primary objective is to provide precise and faithful explanations, represented by the extracted rationales. In this paper, we take a semi-supervised approach to optimize for the plausibility of extracted rationales. We adopt a pre-trained natural language inference (NLI) model and further fine-tune it on a small set of supervised rationales ($10\%$). The NLI predictor is leveraged as a source of supervisory signals to the explainer via entailment alignment. We show that, by enforcing the alignment agreement between the explanation and answer in a question-answering task, the performance can be improved without access to ground truth labels. We evaluate our approach on the ERASER dataset and show that our approach achieves comparable results with supervised extractive models and outperforms unsupervised approaches by $> 100\%$.