Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering

TL;DR

This paper demonstrates that reframing multi-choice question answering as binary classification of question-answer pairs significantly improves performance across various tasks and models, outperforming traditional multi-class approaches.

Contribution

The authors introduce a simple binary classification approach for MCQA tasks, showing it outperforms standard multi-class scoring methods across multiple datasets and models.

Findings

Binary classification approach outperforms traditional multi-class scoring.

DeBERTa achieves top or near-top results on public leaderboards.

Method is effective across diverse question answering tasks.

Abstract

We propose a simple refactoring of multi-choice question answering (MCQA) tasks as a series of binary classifications. The MCQA task is generally performed by scoring each (question, answer) pair normalized over all the pairs, and then selecting the answer from the pair that yield the highest score. For n answer choices, this is equivalent to an n-class classification setup where only one class (true answer) is correct. We instead show that classifying (question, true answer) as positive instances and (question, false answer) as negative instances is significantly more effective across various models and datasets. We show the efficacy of our proposed approach in different tasks -- abductive reasoning, commonsense question answering, science question answering, and sentence completion. Our DeBERTa binary classification model reaches the top or close to the top performance on public leaderboards for these tasks. The source code of the proposed approach is available at https://github.com/declare-lab/TEAM.