There is a growing consensus that, in order to serve as models of human language processing, language models (LMs) need to be constrained in their use of memory for context, the analogue to human working memory (WM).Here we take a novel yet simple approach to constraining WM in language models, in a way that reflects models of human cognition where memory is treated as a limited resource and deployed strategically.In order to capture this constraint on memory encoding, we inject noise into the hidden representations of Transformerbased LMs at tunable rates.Then we train the models with a hybrid objective, such that they learn to maximize the performance of nextword prediction subject to explicit constraints on the total encoding precision.We find that explicit WM constraints improve the model's alignment with human reading times.More importantly, we find that the need to manage encoding precision reshapes the nature of the models' context representations, making them more compressed and categorical.Our results show how resource-rational models of WM allocation can be implemented in neural models simply and successfully, and point to a dissociation between WM retrieval mechanisms and the underlying memory representations in models of human sentence processing.

Combinatory Categorial Grammar (CCG), a lexicalized formalism known for its flexible constituency, is well-suited for modeling headfinal languages with flexible word order like Turkish.Building on Kuzgun et al. (2023), we first develop a Turkish CCG lexicon by automatically inducing categories from a dependency treebank.By leveraging standard and extended operations tailored to Turkish syntax, our parser achieves a robust coverage of 92.5%.Furthermore, we introduce the first (partially) incremental, left-to-right CCG parser for Turkish, designed to facilitate the immediate integration of words into the evolving representation.Finally, we present an example experiment showing that CCG parsers can model psycholinguistic evidence for extra processing costs associated with arguments in noncanonical positions, via the frequency of orderreversing operations.These findings provide evidence that CCG offers a cognitively plausible framework for modeling real-time processing in languages like Turkish. RulesCategory

The evolving socio-technical landscape in peri-urban Tanzania has paved the way for a dramatic increase in smartphone-supported micro and small enterprises. We conduct surveys and focus groups with 46 such entrepreneurs, shedding light on the internal mechanisms and external networks of their businesses. We uncover the new trust dynamics encountered in online interactions, the gendered aspects of this emerging business model, and the means through which people with low capital are reclaiming economic empowerment through entrepreneurship.

Humans exhibit garden path effects: When reading sentences that are temporarily structurally ambiguous, they slow down when the structure is disambiguated in favor of the less preferred alternative.Surprisal theory (Hale, 2001;Levy, 2008), a prominent explanation of this finding, proposes that these slowdowns are due to the unpredictability of each of the words that occur in these sentences.Challenging this hypothesis, van Schijndel and Linzen (2021) find that estimates of the cost of word predictability derived from language models severely underestimate the magnitude of human garden path effects.In this work, we consider whether this underestimation is due to the fact that humans weight syntactic factors in their predictions more highly than language models do.We propose a method for estimating syntactic predictability from a language model, allowing us to weigh the cost of lexical and syntactic predictability independently.We find that treating syntactic predictability independently from lexical predictability indeed results in larger estimates of garden path.At the same time, even when syntactic predictability is independently weighted, surprisal still greatly underestimate the magnitude of human garden path effects.Our results support the hypothesis that predictability is not the only factor responsible for the processing cost associated with garden path sentences.

This paper presents eKichabi, a tool for retrieving contact information for agriculture-related enterprises in Tanzania. eKichabi is an Unstructured Supplementary Service Data (USSD) application which users can access through basic mobile phones. We describe our focus groups, a design iteration, deployment in four villages, and follow up interviews by phone. This work demonstrates the feasibility of USSD for information access applications that have the potential for deployment on a large scale in the developing world. From user interviews, we identified strong evidence of eKichabi fulfilling an unmet need for business related information, both in identifying business contacts in other villages, as well locating specific service providers. One of our key findings demonstrates that users access information through multiple modes, including text search, in addition to menu navigation organized by both business sector category and geographic area.

Tools are used at every stage of the software life cycle with particular recent emphasis on the maintenance period. Evidence shows that maintenance tools are underused, even by the developers who create them. Integrated development environments were created to empower developers, but they have remained virtually unchanged since the late 1990s. This paper examines the challenges of creating development tools, analyzes the usability of two frequently used tools, and suggests that poor tool usability may be inhibiting more efficient software development.