改进词义预测的难度计算，考虑词语间的相似性。
Towards a Similarity-adjusted Surprisal Theory
- 用多样性指数构建考虑词语相似性的新预测度量
- 在部分数据集上比传统方法更能预测阅读时长
- 适合研究语言理解机制的认知科学家
surprisal 理论认为理解一个词所需认知努力由其上下文可预测性决定。传统方法将词语视为独立实体，忽略它们之间的相似性。Giulianelli 等（2023）引入信息价值来衡量可预测性，以考虑交流单元间的相似性。本文基于 Ricotta and Szeidl（2006）的多样性指数，提出相似性调整后的 surprisal，揭示了 surprisal 与信息价值之间的数学关系。当考虑语义梯度相似性时，该指标等价于信息价值；当词语被视为互异时，则退化为标准 surprisal。阅读时间实验显示，相似性调整 surprisal 在某些数据集上比标准 surprisal 具有更强预测能力，表明其是理解努力的互补度量。
原文摘要 · Abstract (English)
Surprisal theory posits that the cognitive effort required to comprehend a word is determined by its contextual predictability, quantified as surprisal. Traditionally, surprisal theory treats words as distinct entities, overlooking any potential similarity between them. Giulianelli et al. (2023) address this limitation by introducing information value, a measure of predictability designed to account for similarities between communicative units. Our work leverages Ricotta and Szeidl's (2006) diversity index to extend surprisal into a metric that we term similarity-adjusted surprisal, exposing a mathematical relationship between surprisal and information value. Similarity-adjusted surprisal aligns with information value when considering graded similarities and reduces to standard surprisal when words are treated as distinct. Experimental results with reading time data indicate that similarity-adjusted surprisal adds predictive power beyond standard surprisal for certain datasets, suggesting it serves as a complementary measure of comprehension effort.
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