TY - GEN
T1 - What did you refer to? Evaluating Co-references in Dialogue
AU - Zhang, Weinan
AU - Zhang, Yue
AU - Tang, Hanlin
AU - Zhao, Zhengyu
AU - Zhu, Caihai
AU - Liu, Ting
N1 - Publisher Copyright:
© 2021 Association for Computational Linguistics
PY - 2021
Y1 - 2021
N2 - Existing neural end-to-end dialogue models have limitations on exactly interpreting the linguistic structures, such as ellipsis, anaphor and co-reference, etc., in dialogue history context. Therefore, it is hard to determine whether the dialogue models truly understand a dialogue or not, only depending on the coherence evaluation of their generated responses. To address these issues, in this paper, we proposed to directly measure the capability of dialogue models on understanding the entity-oriented structures via question answering and construct a new benchmark dataset, DEQA, including large-scale English and Chinese human-human dialogues. Experiments carried on representative dialogue models show that these models all face challenges on the proposed dialogue understanding task. The DEQA dataset will release for research use.
AB - Existing neural end-to-end dialogue models have limitations on exactly interpreting the linguistic structures, such as ellipsis, anaphor and co-reference, etc., in dialogue history context. Therefore, it is hard to determine whether the dialogue models truly understand a dialogue or not, only depending on the coherence evaluation of their generated responses. To address these issues, in this paper, we proposed to directly measure the capability of dialogue models on understanding the entity-oriented structures via question answering and construct a new benchmark dataset, DEQA, including large-scale English and Chinese human-human dialogues. Experiments carried on representative dialogue models show that these models all face challenges on the proposed dialogue understanding task. The DEQA dataset will release for research use.
UR - https://www.scopus.com/pages/publications/85123911759
U2 - 10.18653/v1/2021.findings-acl.450
DO - 10.18653/v1/2021.findings-acl.450
M3 - 会议稿件
AN - SCOPUS:85123911759
T3 - Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
SP - 5075
EP - 5084
BT - Findings of the Association for Computational Linguistics
A2 - Zong, Chengqing
A2 - Xia, Fei
A2 - Li, Wenjie
A2 - Navigli, Roberto
PB - Association for Computational Linguistics (ACL)
T2 - Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Y2 - 1 August 2021 through 6 August 2021
ER -