@inproceedings{mu-li-2024-generating,
title = "Generating Contrastive Narratives Using the Brownian Bridge Process for Narrative Coherence Learning",
author = "Mu, Feiteng and
Li, Wenjie",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.353",
pages = "6538--6555",
abstract = "A major challenge for narrative reasoning is to learn narrative coherence. Existing works mainly follow the contrastive learning paradigm. However, the negative samples in their methods can be easily distinguished, which makes their methods unsatisfactory. In this work, we devise two strategies for mining hard negatives, including (1) crisscrossing a narrative and its contrastive variants; and (2) event-level replacement. To obtain contrastive variants, we utilize the Brownian Bridge process to guarantee the quality of generated contrastive narratives. We evaluate our model on several tasks. The result proves the effectiveness of our method, and shows that our method is applicable to many applications.",
}
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%0 Conference Proceedings
%T Generating Contrastive Narratives Using the Brownian Bridge Process for Narrative Coherence Learning
%A Mu, Feiteng
%A Li, Wenjie
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F mu-li-2024-generating
%X A major challenge for narrative reasoning is to learn narrative coherence. Existing works mainly follow the contrastive learning paradigm. However, the negative samples in their methods can be easily distinguished, which makes their methods unsatisfactory. In this work, we devise two strategies for mining hard negatives, including (1) crisscrossing a narrative and its contrastive variants; and (2) event-level replacement. To obtain contrastive variants, we utilize the Brownian Bridge process to guarantee the quality of generated contrastive narratives. We evaluate our model on several tasks. The result proves the effectiveness of our method, and shows that our method is applicable to many applications.
%U https://aclanthology.org/2024.acl-long.353
%P 6538-6555
Markdown (Informal)
[Generating Contrastive Narratives Using the Brownian Bridge Process for Narrative Coherence Learning](https://aclanthology.org/2024.acl-long.353) (Mu & Li, ACL 2024)
ACL