The abductive natural language inference task ($\alpha$NLI) is proposed to
evaluate the abductive reasoning ability of a learning system. In the
$\alpha$NLI task, two observations are given and the most plausible hypothesis
is asked to pick out from the candidates. Existing methods simply formulate it
as a classification problem, thus a cross-entropy log-loss objective is used
during training. However, discriminating true from false does not measure the
plausibility of a hypothesis, for all the hypotheses have a chance to happen,
only the probabilities are different. To fill this gap, we switch to a ranking
perspective that sorts the hypotheses in order of their plausibilities. With
this new perspective, a novel $L2R^2$ approach is proposed under the
learning-to-rank framework. Firstly, training samples are reorganized into a
ranking form, where two observations and their hypotheses are treated as the
query and a set of candidate documents respectively. Then, an ESIM model or
pre-trained language model, e.g. BERT or RoBERTa, is obtained as the scoring
function. Finally, the loss functions for the ranking task can be either
pair-wise or list-wise for training. The experimental results on the ART
dataset reach the state-of-the-art in the public leaderboard.

本文提出了一种基于排序的 $L2R^2$ 方法，用于解决自然语言推理任务中的假设排序问题，并使用 ESIM 或预训练语言模型（如 BERT 或 RoBERTa）作为评分函数，实验证明，该方法取得了 ART 数据集中最先进的性能水平。

L2R2: 利用排名进行缺失推理

L2R2: Leveraging Ranking for Abductive Reasoning

Recently, biomedical version of embeddings obtained from language models such
as BioELMo have shown state-of-the-art results for the textual inference task
in the medical domain. In this paper, we explore how to incorporate structured
domain knowledge, available in the form of a knowledge graph (UMLS), for the
Medical NLI task. Specifically, we experiment with fusing embeddings obtained
from knowledge graph with the state-of-the-art approaches for NLI task (ESIM
model). We also experiment with fusing the domain-specific sentiment
information for the task. Experiments conducted on MedNLI dataset clearly show
that this strategy improves the baseline BioELMo architecture for the Medical
NLI task.

本文研究如何将医学语言模型（BioELMo）的结构与医学任务中可用的知识图谱（UMLS）进行融合，通过实验验证结论表明融合策略不仅仅是可以改善 MedNLI 数据集上的 BioELMo 模型的基线表现，而且在医疗领域的任务中也导致了最前沿的结果。