# Generating Token-Level Explanations for Natural Language Inference

## TL;DR

This paper introduces a method for generating token-level explanations for Natural Language Inference (NLI) models without requiring explicit annotation, comparing different explanation techniques on the SNLI dataset.

## Contribution

It extends zero-shot explanation methods from single sentences to sentence pairs in NLI and evaluates explanation techniques without needing annotated training data.

## Key findings

- MIL-based method is faster but less accurate
- Black-box explanation methods outperform MIL in accuracy
- Approach works without explicit token-level annotation

## Abstract

The task of Natural Language Inference (NLI) is widely modeled as supervised sentence pair classification. While there has been a lot of work recently on generating explanations of the predictions of classifiers on a single piece of text, there have been no attempts to generate explanations of classifiers operating on pairs of sentences. In this paper, we show that it is possible to generate token-level explanations for NLI without the need for training data explicitly annotated for this purpose. We use a simple LSTM architecture and evaluate both LIME and Anchor explanations for this task. We compare these to a Multiple Instance Learning (MIL) method that uses thresholded attention make token-level predictions. The approach we present in this paper is a novel extension of zero-shot single-sentence tagging to sentence pairs for NLI. We conduct our experiments on the well-studied SNLI dataset that was recently augmented with manually annotation of the tokens that explain the entailment relation. We find that our white-box MIL-based method, while orders of magnitude faster, does not reach the same accuracy as the black-box methods.