论文标题
两个比许多人好吗？二进制分类作为多项选择问题的有效方法
Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering
论文作者
论文摘要
我们提出了一个简单的重构多选项问题答案（MCQA）任务，作为一系列二进制分类。 MCQA任务通常是通过在所有对上对每个（问题，答案）对归一对进行得分来执行的，然后从对获得最高分数的两对中选择答案。对于n个答案选择，这等同于N级分类设置，其中只有一个类（正确答案）是正确的。相反，我们将分类（问题，真实答案）视为积极实例和（问题，错误答案），因为负面实例在各种模型和数据集中更有效。我们显示了我们提出的方法在不同任务中的功效 - 绑架推理，常识性问题回答，科学问题回答和句子完成。我们的Deberta二进制分类模型在这些任务上达到了公共排行榜上最高或接近最高表现。该方法的源代码可在https://github.com/declare-lab/team上获得。
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.
