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

29 Oct 2022arXiv:2210.16495archive 2025-07-28

Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, Soujanya Poria

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.

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configure_optimizer declare-lab/TEAM/train_cicero.py official repository ran MIT (permissive) · 4dab6811065fbe1c · report
configure_dataloaders declare-lab/TEAM/train_cicero.py official repository unverified MIT (permissive) · 25695845c448eef9 · report
configure_dataloaders declare-lab/TEAM/train_cicero2.py official repository unverified MIT (permissive) · eedd86173a2d52d7 · report
configure_dataloaders declare-lab/TEAM/train_cosmosqa.py official repository unverified MIT (permissive) · 5d544b01234bdcee · report
configure_dataloaders declare-lab/TEAM/train_csqa.py official repository unverified MIT (permissive) · 6b021e1d8b19988c · report
configure_dataloaders declare-lab/TEAM/train_csqa2.py official repository unverified MIT (permissive) · ffc49c4218a2e155 · report
configure_scheduler declare-lab/TEAM/train_cicero.py official repository unverified MIT (permissive) · 83ef42f1f5c80dc5 · report

Tasks

Binary ClassificationQuestion AnsweringScience Question AnsweringSentenceSentence Completion

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Question Answering PIQA DeBERTa-Large 304M Accuracy 87.4 #5 of 67 Archive leaderboard report
Question Answering PIQA DeBERTa-Large 304M (classification-based) Accuracy 85.9 #9 of 67 Archive leaderboard report
Question Answering SIQA DeBERTa-Large 304M Accuracy 80.2 #6 of 24 Archive leaderboard report
Question Answering SIQA DeBERTa-Large 304M (classification-based) Accuracy 79.9 #7 of 24 Archive leaderboard report
Sentence Completion HellaSwag DeBERTa-Large 304M (classification-based) Accuracy 95.6 #3 of 89 Archive leaderboard report
Sentence Completion HellaSwag DeBERTa-Large 304M Accuracy 94.7 #6 of 89 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

DeBERTa

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