  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
https://dataset.org/dialogre/.
