Relation extraction (RE) is a fundamental task in natural language
processing, aiming to identify relations between target entities in text. While
many RE methods are designed for a single sentence or document, cross-document
RE has emerged to address relations across multiple long documents. Given the
nature of long documents in cross-document RE, extracting document embeddings
is challenging due to the length constraints of pre-trained language models.
Therefore, we propose REward-based Input Construction (REIC), the first
learning-based sentence selector for cross-document RE. REIC extracts sentences
based on relational evidence, enabling the RE module to effectively infer
relations. Since supervision of evidence sentences is generally unavailable, we
train REIC using reinforcement learning with RE prediction scores as rewards.
Experimental results demonstrate the superiority of our method over heuristic
methods for different RE structures and backbones in cross-document RE. Our
code is publicly available at this https URL

提出了基于奖励的输入构造 (REIC) 方法，用于跨文档关系抽取，通过提取与关系有关的证据句子，使关系模块能够有效地推断关系。实验结果表明，该方法在跨文档关系抽取中的不同结构和骨干上优于启发式方法。

基于奖励的输入构建方法用于跨文档关系抽取

Reward-based Input Construction for Cross-document Relation Extraction

Relation Extraction (RE) is a fundamental task of information extraction,
which has attracted a large amount of research attention. Previous studies
focus on extracting the relations within a sentence or document, while
currently researchers begin to explore cross-document RE. However, current
cross-document RE methods directly utilize text snippets surrounding target
entities in multiple given documents, which brings considerable noisy and
non-relevant sentences. Moreover, they utilize all the text paths in a document
bag in a coarse-grained way, without considering the connections between these
text this http URL this paper, we aim to address both of these shortages and push
the state-of-the-art for cross-document RE. First, we focus on input
construction for our RE model and propose an entity-based document-context
filter to retain useful information in the given documents by using the bridge
entities in the text paths. Second, we propose a cross-document RE model based
on cross-path entity relation attention, which allow the entity relations
across text paths to interact with each other. We compare our cross-document RE
method with the state-of-the-art methods in the dataset CodRED. Our method
outperforms them by at least 10% in F1, thus demonstrating its effectiveness.

本论文提出基于实体的文档上下文过滤来构建输入并基于跨路径实体关系注意力的交叉文档关系提取模型，与现有方法比较，在 CodRED 数据集上取得至少 10％ 的 F1 值的提升，从而展示其在跨文档关系提取中的有效性。