Eleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re, Tianyang Xu, Mario Giulianelli, Karolina Stanczak, Ryan Cotterell. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Eleftheria Tsipidi, Samuel Kiegeland, Franz Nowak, Tianyang Xu, Ethan Wilcox, Alex Warstadt, Ryan Cotterell, Mario Giulianelli. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

The Uniform Information Density (UID) hypothesis posits that speakers tend to distribute information evenly across linguistic units to achieve efficient communication.Of course, information rate in texts and discourses is not perfectly uniform.While these fluctuations can be viewed as theoretically uninteresting noise on top of a uniform target, another explanation is that UID is not the only functional pressure regulating information content in a language.Speakers may also seek to maintain interest, adhere to writing conventions, and build compelling arguments.In this paper, we propose one such functional pressure; namely that speakers modulate information rate based on location within a hierarchically-structured model of discourse.We term this the Structured Context Hypothesis and test it by predicting the surprisal contours of naturally occurring discourses extracted from large language models using predictors derived from discourse structure.We find that hierarchical predictors are significant predictors of a discourse's information contour and that deeply nested hierarchical predictors are more predictive than shallow ones.This work takes an initial step beyond UID to propose testable hypotheses for why the information rate fluctuates in predictable ways.https://github.com/rycolab/ surprisal-discourse

The left-corner transformation (Rosenkrantz and Lewis, 1970) is used to remove left recursion from context-free grammars, which is an important step towards making the grammar parsable top-down with simple techniques.This paper generalizes prior left-corner transformations to support semiring-weighted production rules and to provide finer-grained control over which left corners may be moved.Our generalized left-corner transformation (GLCT) arose from unifying the left-corner transformation and speculation transformation (Eisner and Blatz, 2007), originally for logic programming.Our new transformation and speculation define equivalent weighted languages.Yet, their derivation trees are structurally different in an important way: GLCT replaces left recursion with right recursion, and speculation does not.We also provide several technical results regarding the formal relationships between the outputs of GLCT, speculation, and the original grammar.Lastly, we empirically investigate the efficiency of GLCT for left-recursion elimination from grammars of nine languages.