Finding Spoiler Bias in Tweets by Zero-shot Learning and Knowledge Distilling from Neural Text Simplification

Avi Bleiweiss


Abstract
Automatic detection of critical plot information in reviews of media items poses unique challenges to both social computing and computational linguistics. In this paper we propose to cast the problem of discovering spoiler bias in online discourse as a text simplification task. We conjecture that for an item-user pair, the simpler the user review we learn from an item summary the higher its likelihood to present a spoiler. Our neural model incorporates the advanced transformer network to rank the severity of a spoiler in user tweets. We constructed a sustainable high-quality movie dataset scraped from unsolicited review tweets and paired with a title summary and meta-data extracted from a movie specific domain. To a large extent, our quantitative and qualitative results weigh in on the performance impact of named entity presence in plot summaries. Pretrained on a split-and-rephrase corpus with knowledge distilled from English Wikipedia and fine-tuned on our movie dataset, our neural model shows to outperform both a language modeler and monolingual translation baselines.
Anthology ID:
2021.ltedi-1.7
Volume:
Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion
Month:
April
Year:
2021
Address:
Kyiv
Editors:
Bharathi Raja Chakravarthi, John P. McCrae, Manel Zarrouk, Kalika Bali, Paul Buitelaar
Venue:
LTEDI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
51–60
Language:
URL:
https://aclanthology.org/2021.ltedi-1.7
DOI:
Bibkey:
Cite (ACL):
Avi Bleiweiss. 2021. Finding Spoiler Bias in Tweets by Zero-shot Learning and Knowledge Distilling from Neural Text Simplification. In Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion, pages 51–60, Kyiv. Association for Computational Linguistics.
Cite (Informal):
Finding Spoiler Bias in Tweets by Zero-shot Learning and Knowledge Distilling from Neural Text Simplification (Bleiweiss, LTEDI 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2021.ltedi-1.7.pdf
Dataset:
 2021.ltedi-1.7.Dataset.zip
Code
 bshalem/mst