THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for
  CTR-based Textual Entailment and Evidence Retrieval
Yuxuan Zhou Ziyu Jin Meiwei Li Miao Li Xien Liu Xinxin You Ji Wu
The NLI4CT task aims to entail hypotheses based on Clinical Trial Reports
(CTRs) and retrieve the corresponding evidence supporting the justification.
This task poses a significant challenge, as verifying hypotheses in the NLI4CT
task requires the integration of multiple pieces of evidence from one or two
CTR(s) and the application of diverse levels of reasoning, including textual
and numerical. To address these problems, we present a multi-granularity system
for CTR-based textual entailment and evidence retrieval in this paper.
Specifically, we construct a Multi-granularity Inference Network (MGNet) that
exploits sentence-level and token-level encoding to handle both textual
entailment and evidence retrieval tasks. Moreover, we enhance the numerical
inference capability of the system by leveraging a T5-based model, SciFive,
which is pre-trained on the medical corpus. Model ensembling and a joint
inference method are further utilized in the system to increase the stability
and consistency of inference. The system achieves f1-scores of 0.856 and 0.853
on textual entailment and evidence retrieval tasks, resulting in the best
performance on both subtasks. The experimental results corroborate the
effectiveness of our proposed method. Our code is publicly available at
https://github.com/THUMLP/NLI4CT.