Pre-trained Generative models such as BART, T5, etc. have gained prominence
as a preferred method for text generation in various natural language
processing tasks, including abstractive long-form question answering (QA) and
summarization. However, the potential of generative models in extractive QA
tasks, where discriminative models are commonly employed, remains largely
unexplored. Discriminative models often encounter challenges associated with
label sparsity, particularly when only a small portion of the context contains
the answer. The challenge is more pronounced for multi-span answers. In this
work, we introduce a novel approach that uses the power of pre-trained
generative models to address extractive QA tasks by generating indexes
corresponding to context tokens or sentences that form part of the answer.
Through comprehensive evaluations on multiple extractive QA datasets, including
MultiSpanQA, BioASQ, MASHQA, and WikiQA, we demonstrate the superior
performance of our proposed approach compared to existing state-of-the-art
models.

本文介绍了一种新颖的方法，利用预训练的生成模型来解决抽取式問答任务，通过生成与回答的上下文标记或句子相对应的索引，实现了在多个抽取式問答数据集上优于现有 state-of-the-art 模型的卓越性能。