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    <abstract>When reading a literary piece, readers often make inferences about various characters’ roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU – a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.</abstract>
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%0 Conference Proceedings
%T “Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
%A Brahman, Faeze
%A Huang, Meng
%A Tafjord, Oyvind
%A Zhao, Chao
%A Sachan, Mrinmaya
%A Chaturvedi, Snigdha
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F brahman-etal-2021-characters-tell
%X When reading a literary piece, readers often make inferences about various characters’ roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU – a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.
%R 10.18653/v1/2021.findings-emnlp.150
%U https://aclanthology.org/2021.findings-emnlp.150/
%U https://doi.org/10.18653/v1/2021.findings-emnlp.150
%P 1734-1752

Markdown (Informal)

[“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding](https://aclanthology.org/2021.findings-emnlp.150/) (Brahman et al., Findings 2021)

• “Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding (Brahman et al., Findings 2021)

ACL

• Faeze Brahman, Meng Huang, Oyvind Tafjord, Chao Zhao, Mrinmaya Sachan, and Snigdha Chaturvedi. 2021. “Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1734–1752, Punta Cana, Dominican Republic. Association for Computational Linguistics.