Document-level relation extraction (DocRE) aims to determine the relation
between two entities from a document of multiple sentences. Recent studies
typically represent the entire document by sequence- or graph-based models to
predict the relations of all entity pairs. However, we find that such a model
is not robust and exhibits bizarre behaviors: it predicts correctly when an
entire test document is fed as input, but errs when non-evidence sentences are
removed. To this end, we propose a Sentence Importance Estimation and Focusing
(SIEF) framework for DocRE, where we design a sentence importance score and a
sentence focusing loss, encouraging DocRE models to focus on evidence
sentences. Experimental results on two domains show that our SIEF not only
improves overall performance, but also makes DocRE models more robust.
Moreover, SIEF is a general framework, shown to be effective when combined with
a variety of base DocRE models.

提出了一种用于 DocRE 的 SIEF 框架，通过设计句子重要性评分和句子聚焦损失来鼓励 DocRE 模型专注于证据句子，以提高整体性能和鲁棒性。

使用句子重要性评估和聚焦进行文档级关系抽取

Document-Level Relation Extraction with Sentences Importance Estimation and Focusing

Document-level Relation Extraction (RE) is a more challenging task than
sentence RE as it often requires reasoning over multiple sentences. Yet, human
annotators usually use a small number of sentences to identify the relationship
between a given entity pair. In this paper, we present an embarrassingly simple
but effective method to heuristically select evidence sentences for
document-level RE, which can be easily combined with BiLSTM to achieve good
performance on benchmark datasets, even better than fancy graph neural network
based methods. We have released our code at
this https URL

该论文提出了一种简单而有效的文档级关系抽取方法，可结合 BiLSTM 使用少量证据句子识别给定实体对之间的关系，并在基准数据集上取得了优异的性能，甚至优于基于图神经网络的方法。

只需三句话：本地路径增强的文档关系提取

Three Sentences Are All You Need: Local Path Enhanced Document Relation  Extraction

Remarkable success has been achieved in the last few years on some limited
machine reading comprehension (MRC) tasks. However, it is still difficult to
interpret the predictions of existing MRC models. In this paper, we focus on
extracting evidence sentences that can explain or support the answers of
multiple-choice MRC tasks, where the majority of answer options cannot be
directly extracted from reference documents.
Due to the lack of ground truth evidence sentence labels in most cases, we
apply distant supervision to generate imperfect labels and then use them to
train an evidence sentence extractor. To denoise the noisy labels, we apply a
recently proposed deep probabilistic logic learning framework to incorporate
both sentence-level and cross-sentence linguistic indicators for indirect
supervision. We feed the extracted evidence sentences into existing MRC models
and evaluate the end-to-end performance on three challenging multiple-choice
MRC datasets: MultiRC, RACE, and DREAM, achieving comparable or better
performance than the same models that take as input the full reference
document. To the best of our knowledge, this is the first work extracting
evidence sentences for multiple-choice MRC.

本研究提出一种利用深度概率逻辑学习框架提取证据句子的方法，在三个具有挑战性的多项选择机器阅读理解数据集上实现了可比较或更好的性能，为多项选择机器阅读理解提供了新的思路。