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Found 14 papers, 7 papers with code
Sample Relationship from Learning Dynamics Matters for Generalisation
Although much research has been done on proposing new models or loss functions to improve the generalisation of artificial neural networks (ANNs), less attention has been directed to the impact of the training data on generalisation.
Reliable Detection and Quantification of Selective Forces in Language Change
Language change is a cultural evolutionary process in which variants of linguistic variables change in frequency through processes analogous to mutation, selection and genetic drift.
Expressivity of Emergent Languages is a Trade-off between Contextual Complexity and Unpredictability
Researchers are using deep learning models to explore the emergence of language in various language games, where simulated agents interact and develop an emergent language to solve a task.
Meta-Learning to Compositionally Generalize
Natural language is compositional; the meaning of a sentence is a function of the meaning of its parts.
Expressivity of Emergent Language is a Trade-off between Contextual Complexity and Unpredictability
Researchers are using deep learning models to explore the emergence of language in various language games, where agents interact and develop an emergent language to solve tasks.
From partners to populations: A hierarchical Bayesian account of coordination and convention
Languages are powerful solutions to coordination problems: they provide stable, shared expectations about how the words we say correspond to the beliefs and intentions in our heads.
Conceptual similarity and communicative need shape colexification: an experimental study
Colexification refers to the phenomenon of multiple meanings sharing one word in a language.
Communicative need modulates competition in language change
By contrast, in topics which are increasing in importance for language users, near-synonymous words tend not to compete directly and can coexist.
The Emergence of Compositional Languages for Numeric Concepts Through Iterated Learning in Neural Agents
Since first introduced, computer simulation has been an increasingly important tool in evolutionary linguistics.
Challenges in detecting evolutionary forces in language change using diachronic corpora
Newberry et al. (Detecting evolutionary forces in language change, Nature 551, 2017) tackle an important but difficult problem in linguistics, the testing of selective theories of language change against a null model of drift.
Quantifying the dynamics of topical fluctuations in language
In this work, we introduce a simple model for controlling for topical fluctuations in corpora - the topical-cultural advection model - and demonstrate how it provides a robust baseline of variability in word frequency changes over time.
The cognitive roots of regularization in language
Regularization occurs when the output a learner produces is less variable than the linguistic data they observed.
Word learning under infinite uncertainty
Language learners must learn the meanings of many thousands of words, despite those words occurring in complex environments in which infinitely many meanings might be inferred by the learner as a word's true meaning.
Stochastic dynamics of lexicon learning in an uncertain and nonuniform world
We study the time taken by a language learner to correctly identify the meaning of all words in a lexicon under conditions where many plausible meanings can be inferred whenever a word is uttered.
