  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 https://github.com/phoenixsecularbird/R2F.
