Document-level natural language inference (DOCNLI) is a new challenging task
in natural language processing, aiming at judging the entailment relationship
between a pair of hypothesis and premise documents. Current datasets and
baselines largely follow sentence-level settings, but fail to address the
issues raised by longer documents. In this paper, we establish a general
solution, named Retrieval, Reading and Fusion (R2F) framework, and a new
setting, by analyzing the main challenges of DOCNLI: interpretability,
long-range dependency, and cross-sentence inference. The basic idea of the
framework is to simplify document-level task into a set of sentence-level
tasks, and improve both performance and interpretability with the power of
evidence. For each hypothesis sentence, the framework retrieves evidence
sentences from the premise, and reads to estimate its credibility. Then the
sentence-level results are fused to judge the relationship between the
documents. For the setting, we contribute complementary evidence and entailment
label annotation on hypothesis sentences, for interpretability study. Our
experimental results show that R2F framework can obtain state-of-the-art
performance and is robust for diverse evidence retrieval methods. Moreover, it
can give more interpretable prediction results. Our model and code are released
at this https URL.

本论文提出了 R2F 框架来解决 DOCLNI 的主要挑战：可解释性、长跨度依赖和跨句子推理，并支持多样化的证据检索方法，为每个假设语句从前提中检索出相应的证据语句并进行可信度评估，最后将句子级的结果融合来判断文档之间的关系，实验结果表明 R2F 框架可以获得最先进的性能，而且可以给出更可解释的预测结果。