Expect the Unexpected? Testing the Surprisal of Salient Entities
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Abstract (arXiv)
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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References · 12
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
- 2021Revisiting the Uniform Information Density Hypothesiscited by 68
- 2018SCORES: Shape Composition with Recursive Substructure Priorscited by 24
- 2021OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genrescited by 18
- 2023A Cross-Linguistic Pressure for Uniform Information Density in Word Ordercited by 17
- 2024Surprise! Uniform Information Density Isn't the Whole Story: Predicting Surprisal Contours in Long-form Discoursecited by 17
- 2021Disambiguatory Signals are Stronger in Word-initial Positionscited by 9
- 2021WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genrescited by 9
- 2025GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Predictioncited by 6
- 2024GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domainscited by 5
- 2026Is Information Density Uniform when Utterances are Grounded on Perception and Discourse?cited by 3
- 2026Not Worth Mentioning? A Pilot Study on Salient Proposition Annotationcited by 2
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