Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering
This provides a simple but effective solution for multi-choice question answering tasks, applicable across domains like commonsense reasoning and science QA, though it is an incremental improvement over existing classification approaches.
The paper tackles multi-choice question answering by reframing it as binary classification of question-answer pairs, showing this approach significantly outperforms traditional multi-class methods across various models and datasets, with their DeBERTa model achieving top or near-top performance on public leaderboards.
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.