Expect the Unexpected? Testing the Surprisal of Salient Entities
Jessica Lin, Amir Zeldes
摘要
Previous work examining the Uniform Information Density (UID) hypothesis has shown that while information as measured by surprisal metrics is distributed more or less evenly across documents overall, local discrepancies can arise due to functional pressures corresponding to syntactic and discourse structural constraints. However, work thus far has largely disregarded the relative salience of discourse participants. We fill this gap by studying how overall salience of entities in discourse relates to surprisal using 70K manually annotated mentions across 16 genres of English and a novel minimal-pair prompting method. Our results show that globally salient entities exhibit significantly higher surprisal than non-salient ones, even controlling for position, length, and nesting confounds. Moreover, salient entities systematically reduce surprisal for surrounding content when used as prompts, enhancing document-level predictability. This effect varies by genre, appearing strongest in topic-coherent texts and weakest in conversational contexts. Our findings refine the UID competing pressures framework by identifying global entity salience as a mechanism shaping information distribution in discourse.
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它引用的顶会 Paper3
- Revisiting the Uniform Information Density HypothesisClara Meister, Tiago Pimentel, Patrick Haller, Lena A. Jäger 等EMNLP 2021 · 被引用 4 次
- Surprise! Uniform Information Density Isn't the Whole Story: Predicting Surprisal Contours in Long-form DiscourseEleftheria Tsipidi, Franz Nowak, Ryan Cotterell, Ethan Wilcox 等EMNLP 2024 · 被引用 2 次
- GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and DomainsYang Janet Liu, Tatsuya Aoyama, Wesley Scivetti, Yilun Zhu 等EMNLP 2024
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