论文标题
答案之前的线索：生成增强的多项选择质量质量
Clues Before Answers: Generation-Enhanced Multiple-Choice QA
论文作者
论文摘要
多项选择性问题答案（MCQA）的趋势范式正在使用文本到文本框架。通过将不同任务中的数据统一为单个文本到文本格式，它训练了一个既强大又通用的生成编码模型。但是，扭曲生成目标以适合MCQA的分类性质的副作用是解码器的利用不足和可以解码的知识。为了利用预先训练的编码模型的生成能力和潜在的知识，在本文中，我们提出了一种名为GENMC的生成增强的MCQA模型。它从问题中产生了线索，然后利用线索来增强MCQA的读者。它的表现优于多个MCQA数据集上的文本到文本模型。
A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework. By unifying data in different tasks into a single text-to-text format, it trains a generative encoder-decoder model which is both powerful and universal. However, a side effect of twisting a generation target to fit the classification nature of MCQA is the under-utilization of the decoder and the knowledge that can be decoded. To exploit the generation capability and underlying knowledge of a pre-trained encoder-decoder model, in this paper, we propose a generation-enhanced MCQA model named GenMC. It generates a clue from the question and then leverages the clue to enhance a reader for MCQA. It outperforms text-to-text models on multiple MCQA datasets.
