Dialogue-Based Relation Extraction
Dian Yu, Kai Sun, Claire Cardie, Dong Yu · 2020
We present the first human-annotated dialoguebased relation extraction (RE) dataset Dialo-gRE, 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 https:// dataset.org/dialogre/.