Long-context question answering (QA) tasks require reasoning over a long
document or multiple documents. Addressing these tasks often benefits from
identifying a set of evidence spans (e.g., sentences), which provide supporting
evidence for answering the question. In this work, we propose a novel method
for equipping long-context QA models with an additional sequence-level
objective for better identification of the supporting evidence. We achieve this
via an additional contrastive supervision signal in finetuning, where the model
is encouraged to explicitly discriminate supporting evidence sentences from
negative ones by maximizing question-evidence similarity. The proposed
additional loss exhibits consistent improvements on three different strong
long-context transformer models, across two challenging question answering
benchmarks -- HotpotQA and QAsper.

本文提出一种新的方法，通过在 fine-tuning 过程中为模型提供对比监督信号，通过最大化问题 - 证据的相似性，明确区分支持证据句子和负面证据句子，从而为长上下文问答模型提供更好的支持证据识别，取得了一致的性能提升。