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。

基于预训练生成模型的问题回答适应性

Adapting Pre-trained Generative Models for Extractive Question Answering

Knowledge Grounded Conversation Models (KGCM) are usually based on a
selection/retrieval module and a generation module, trained separately or
simultaneously, with or without having access to a gold knowledge option. With
the introduction of large pre-trained generative models, the selection and
generation part have become more and more entangled, shifting the focus towards
enhancing knowledge incorporation (from multiple sources) instead of trying to
pick the best knowledge option. These approaches however depend on knowledge
labels and/or a separate dense retriever for their best performance. In this
work we study the unsupervised selection abilities of pre-trained generative
models (e.g. BART) and show that by adding a score-and-aggregate module between
encoder and decoder, they are capable of learning to pick the proper knowledge
through minimising the language modelling loss (i.e. without having access to
knowledge labels). Trained as such, our model - K-Mine - shows competitive
selection and generation performance against models that benefit from knowledge
labels and/or separate dense retriever.

本文研究了预训练生成模型的无监督选择能力，通过在编码器和解码器之间添加得分和聚合模块。这个模型 - K-Mine - 在没有知识标签和 / 或分离的密集检索器的情况下展示了有竞争力的选择和生成性能。