Aditya Yadavalli University of California, San Diego, Tiago Pimentel Department of Computer Science, ETHZ - ETH Zurich, Tamar I Regev Massachusetts Institute of Technology, Ethan Gotlieb Wilcox, Alex Warstadt University of California, San Diego
Prosody—the melody of speech—conveys critical information often not captured by the words or text of a message.In this paper, we propose an information-theoretic approach to quantify how much is conveyed by prosody that is not recoverable from text alone, and, crucially, what prosody conveys.Our approach applies large speech and language models to estimate the mutual information between a particular dimension of an utterance’s meaning (e.g., its emotion) and any of its communication channels (e.g., audio or text).We then use this approach to quantify the information conveyed by audio and text about sarcasm, emotion, and questionhood, using speech from television and podcasts.We find that for sarcasm and emotion, the audio channel, and by implication the prosodic channel, transmits over an order of magnitude more information about these features than the text channel alone, at least when long-term context beyond the current sentence is unavailable.For questionhood, prosody provides comparatively less additional information.We conclude by outlining a program applying our approach to more dimensions of meaning, communication channels, and languages.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Tatsuki Kuribayashi Mohamed bin Zayed University of Artificial Intelligence, Alex Warstadt University of California, San Diego, Yohei Oseki National Institute of Informatics and University of Tokyo, Ethan Gotlieb Wilcox
A recent study (CITATION) has shown that human sentence processing behavior, typically measured on syntactically unchallenging constructions, can be effectively modeled using surprisal from early layers of large language models (LLMs). This raises the question of whether such advantages of internal layers extend to more syntactically challenging constructions, where surprisal has been reported to underestimate human cognitive effort.In this paper, we begin by exploring internal layers that better estimate human cognitive effort observed in syntactic ambiguity processing in English.Our experiments show that, in contrast to naturalistic reading, later layers better estimate such a cognitive effort, but still underestimate the human data. This dual alignment sheds light on different modes of sentence processing in humans and LMs: naturalistic reading employs a somewhat weak prediction akin to earlier layers of LMs, while syntactically challenging processing requires more fully-contextualized representations, better modeled by later layers of LMs.Motivated by these findings, we also explore several probability-update measures using shallow and deep layers of LMs, showing a complementary advantage to single-layer’s surprisal in reading time modeling.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Xiulin Yang, Heidi R. Getz Georgetown University, Ethan Gotlieb Wilcox
What statistical properties might support learning abstract grammatical knowledge from linear input? We address this question by examining the statistical distribution of function words. Function words have been argued to aid acquisition through three distributional properties: high frequency, reliable syntactic association, and phrase-boundary alignment. We conduct a cross-linguistic corpus analysis of 186 languages, which confirms that all three properties are universal. Using counterfactual language modeling and ablation experiments on English, we show that preserving these properties facilitates acquisition in neural learners, with a Goldilocks effect: function words must be frequent enough to be reliable, yet diverse enough to remain informative to structural dependency. Probing analyses further reveal that different learning conditions produce systematically different reliance on function words.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表