TY - GEN
T1 - TellMeWhy
T2 - Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
AU - Lal, Yash Kumar
AU - Chambers, Nathanael
AU - Mooney, Raymond
AU - Balasubramanian, Niranjan
N1 - Publisher Copyright:
© 2021 Association for Computational Linguistics
PY - 2021
Y1 - 2021
N2 - Answering questions about why characters perform certain actions is central to understanding and reasoning about narratives. Despite recent progress in QA, it is not clear if existing models have the ability to answer “why” questions that may require commonsense knowledge external to the input narrative. In this work, we introduce TellMeWhy, a new crowd-sourced dataset that consists of more than 30k questions and free-form answers concerning why characters in short narratives perform the actions described. For a third of this dataset, the answers are not present within the narrative. Given the limitations of automated evaluation for this task, we also present a systematized human evaluation interface for this dataset. Our evaluation of state-of-the-art models show that they are far below human performance on answering such questions. They are especially worse on questions whose answers are external to the narrative, thus providing a challenge for future QA and narrative understanding research.
AB - Answering questions about why characters perform certain actions is central to understanding and reasoning about narratives. Despite recent progress in QA, it is not clear if existing models have the ability to answer “why” questions that may require commonsense knowledge external to the input narrative. In this work, we introduce TellMeWhy, a new crowd-sourced dataset that consists of more than 30k questions and free-form answers concerning why characters in short narratives perform the actions described. For a third of this dataset, the answers are not present within the narrative. Given the limitations of automated evaluation for this task, we also present a systematized human evaluation interface for this dataset. Our evaluation of state-of-the-art models show that they are far below human performance on answering such questions. They are especially worse on questions whose answers are external to the narrative, thus providing a challenge for future QA and narrative understanding research.
UR - https://www.scopus.com/pages/publications/85123958858
U2 - 10.18653/v1/2021.findings-acl.53
DO - 10.18653/v1/2021.findings-acl.53
M3 - Conference contribution
AN - SCOPUS:85123958858
T3 - Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
SP - 596
EP - 610
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)
Y2 - 1 August 2021 through 6 August 2021
ER -