Existing research studies on cross-sentence relation extraction in long-form
multi-party conversations aim to improve relation extraction without
considering the explainability of such methods. This work addresses that gap by
focusing on extracting explanations that indicate that a relation exists while
using only partially labeled data. We propose our model-agnostic framework,
D-REX, a policy-guided semi-supervised algorithm that explains and ranks
relations. We frame relation extraction as a re-ranking task and include
relation- and entity-specific explanations as an intermediate step of the
inference process. We find that about 90% of the time, human annotators prefer
D-REX's explanations over a strong BERT-based joint relation extraction and
explanation model. Finally, our evaluations on a dialogue relation extraction
dataset show that our method is simple yet effective and achieves a
state-of-the-art F1 score on relation extraction, improving upon existing
methods by 13.5%.

本研究采用半监督算法 D-REX，以中间步骤的关系和实体特定解释为桥梁，将关系提取视为重新排序任务，并在对话关系提取数据集上进行评估，取得了 13.5% 的最优 F1 得分。

D-REX: 带有解释的对话关系提取

D-REX: Dialogue Relation Extraction with Explanations

We present the first human-annotated dialogue-based relation extraction (RE)
dataset DialogRE, aiming to support the prediction of relation(s) between two
arguments that appear in a dialogue. We further offer DialogRE as a platform
for studying cross-sentence RE as most facts span multiple sentences. We argue
that speaker-related information plays a critical role in the proposed task,
based on an analysis of similarities and differences between dialogue-based and
traditional RE tasks. Considering the timeliness of communication in a
dialogue, we design a new metric to evaluate the performance of RE methods in a
conversational setting and investigate the performance of several
representative RE methods on DialogRE. Experimental results demonstrate that a
speaker-aware extension on the best-performing model leads to gains in both the
standard and conversational evaluation settings. DialogRE is available at
this https URL

本文介绍了第一个人工注释的基于对话的关系抽取（RE）数据集 DialogRE，用于支持在对话中预测两个参数之间的关系，尤其是涉及多个句子的跨句子 RE 任务，并基于分析指出演讲者相关信息在该任务中发挥了关键作用。在提出一个新的评估指标以评估在会话环境中进行 RE 方法的性能之后，本文研究了几种常用的 RE 方法在 DialogRE 数据集上的表现，并通过实验证明，最佳表现模型的扩展可以在标准和会话环境的评估设置中都取得收益。