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Found 20 papers, 8 papers with code
SIGMORPHON–UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection
The 2022 SIGMORPHON–UniMorph shared task on large scale morphological inflection generation included a wide range of typologically diverse languages: 33 languages from 11 top-level language families: Arabic (Modern Standard), Assamese, Braj, Chukchi, Eastern Armenian, Evenki, Georgian, Gothic, Gujarati, Hebrew, Hungarian, Itelmen, Karelian, Kazakh, Ket, Khalkha Mongolian, Kholosi, Korean, Lamahalot, Low German, Ludic, Magahi, Middle Low German, Old English, Old High German, Old Norse, Polish, Pomak, Slovak, Turkish, Upper Sorbian, Veps, and Xibe.
Modeling the Relationship between Input Distributions and Learning Trajectories with the Tolerance Principle
Child language learners develop with remarkable uniformity, both in their learning trajectories and ultimate outcomes, despite major differences in their learning environments.
Language Acquisition, Neutral Change, and Diachronic Trends in Noun Classifiers
Languages around the world employ classifier systems as a method of semantic organization and categorization.
SIGMORPHON–UniMorph 2022 Shared Task 0: Modeling Inflection in Language Acquisition
This year’s iteration of the SIGMORPHONUniMorph shared task on “human-like” morphological inflection generation focuses on generalization and errors in language acquisition.
Evaluating LLMs with Multiple Problems at once: A New Paradigm for Probing LLM Capabilities
We find that LLMs are competent multi-problem solvers: they generally perform (nearly) as well on multi-problem tasks as on single-problem tasks.
Evaluating Neural Language Models as Cognitive Models of Language Acquisition
The success of neural language models (LMs) on many technological tasks has brought about their potential relevance as scientific theories of language despite some clear differences between LM training and child language acquisition.
Exploring Linguistic Probes for Morphological Generalization
Modern work on the cross-linguistic computational modeling of morphological inflection has typically employed language-independent data splitting algorithms.
Why Linguistics Will Thrive in the 21st Century: A Reply to Piantadosi (2023)
We present a critical assessment of Piantadosi's (2023) claim that "Modern language models refute Chomsky's approach to language," focusing on four main points.
Morphological Inflection: A Reality Check
Morphological inflection is a popular task in sub-word NLP with both practical and cognitive applications.
The Greedy and Recursive Search for Morphological Productivity
As children acquire the knowledge of their language's morphology, they invariably discover the productive processes that can generalize to new words.
Modeling Morphological Typology for Unsupervised Learning of Language Morphology
This paper describes a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphological typology.
Overestimation of Syntactic Representation in Neural Language Models
With the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so successful.
Morphological Segmentation for Low Resource Languages
This paper describes a new morphology resource created by Linguistic Data Consortium and the University of Pennsylvania for the DARPA LORELEI Program.
Overestimation of Syntactic Representationin Neural Language Models
With the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so successful.
Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages
Generating the English transliteration of a name written in a foreign script is an important and challenging step in multilingual knowledge acquisition and information extraction.
A Framework for Representing Language Acquisition in a Population Setting
Language variation and change are driven both by individuals{'} internal cognitive processes and by the social structures through which language propagates.
