While natural languages differ widely in both canonical word order and word order flexibility, their word orders still follow shared cross-linguistic statistical patterns, often attributed to functional pressures. In the effort to identify these pressures, prior work has compared real and counterfactual word orders. Yet one functional pressure has been overlooked in such investigations: the uniform information density (UID) hypothesis, which holds that information should be spread evenly throughout an utterance. Here, we ask whether a pressure for UID may have influenced word order patterns cross-linguistically. To this end, we use computational models to test whether real orders lead to greater information uniformity than counterfactual orders. In our empirical study of 10 typologically diverse languages, we find that: (i) among SVO languages, real word orders consistently have greater uniformity than reverse word orders, and (ii) only linguistically implausible counterfactual orders consistently exceed the uniformity of real orders. These findings are compatible with a pressure for information uniformity in the development and usage of natural languages.

A Cross-Linguistic Pressure for Uniform Information Density in Word Order

When a language offers multiple options for expressing the same meaning, what principles govern a speaker's choice? Two well-known principles proposed for explaining wide-ranging speaker preference are Uniform Information Density and Availability-Based Production. Here we test the predictions of these theories in a previously uninvestigated case of speaker choice.
Russian has two ways of expressing the comparative: an \textsc{explicit} option (\textit{Ona bystree chem ja}/She fast{\sc-comp} than me{\sc-nom}) and a \textsc{genitive} option (\textit{Ona bystree menya/She fast{\sc-comp} me{\sc-gen}}). We lay out several potential predictions of each theory for speaker choice in the Russian comparative construction, including effects of post-comparative word predictability, phrase length, syntactic complexity, and semantic association between the comparative adjective and subsequent noun. In a corpus study, we find that the explicit construction is used preferentially when the post-comparative noun phrase is longer, has a relative clause, and is less semantically associated with the comparative adjective. A follow-up production experiment using visual scene stimuli to elicit comparative sentences replicates the corpus finding that Russian native speakers prefer the explicit form when post-comparative phrases are longer. These findings offer no clear support for the predictions of Uniform Information Density, but are broadly supportive of Availability-Based Production, with the explicit option serving as an unreduced form that eases speakers' planning of complex or low-availability utterances. Code for this study is available at https://github.mit.edu/thclark/russian_uid


Evidence for Availability Effects on Speaker Choice in the Russian Comparative Alternation

Numerous analyses of reading time (RT) data have been implemented--all in an effort to better understand the cognitive processes driving reading comprehension. 
However, data measured on words at the end of a sentence--or even at the end of a clause--is often omitted due to the confounding factors introduced by so-called "wrap-up effects," which manifests as a skewed distribution of RTs for these words. 
Consequently, the understanding of the cognitive processes that might be involved in these wrap-up effects is limited. In this work, we attempt to learn more about these processes by examining the relationship between wrap-up effects and information-theoretic quantities, such as word and context surprisals. We find that the distribution of information in prior contexts is often predictive of sentence- and clause-final RTs (while not of sentence-medial RTs). This lends support to several prior hypotheses about the processes involved in wrap-up effects.

Analyzing Wrap-Up Effects through an Information-Theoretic Lens

What makes some sentences more memorable than others? In this work, we build on past work in image and word memorability and conduct a large-scale sentence memorability experiment with responses from 500 participants for 2,500 target sentences. We find that sentences vary in their intrinsic memorability. We predict the memorability of sentences from a variety of word- and sentence-level features, including representations from artificial large language models and the information-theoretic measure of surprisal. We find that the best predictor of sentence memorability is the presence of individually memorable words, but that in the absence of such words, sentence-level features predict sentence memorability. This suggests that otherwise forgettable words can together create memorable compositional meanings that remain in memory.

Context-sensitive features predict sentence memorability in the absence of memorable words

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**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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Yuji Matsumoto (RIKEN AIP) 
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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

Poster Session 5: Language processing, representation, and use

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
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CogSci 2022

The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





Posters: Linguistic Theories, Cognitive Modeling and Psycholinguistics

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

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CogSci 2023

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Thomas Clark

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SHORT BIO

Presentations

A Cross-Linguistic Pressure for Uniform Information Density in Word Order

Evidence for Availability Effects on Speaker Choice in the Russian Comparative Alternation

Analyzing Wrap-Up Effects through an Information-Theoretic Lens

Context-sensitive features predict sentence memorability in the absence of memorable words

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