no code implementations • 31 Oct 2023 • Héctor Javier Vázquez Martínez, Annika Lea Heuser, Charles Yang, Jordan Kodner
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.
2 code implementations • 12 May 2021 • Caleb Belth, Sarah Payne, Deniz Beser, Jordan Kodner, Charles Yang
As children acquire the knowledge of their language's morphology, they invariably discover the productive processes that can generalize to new words.
1 code implementation • 7 May 2021 • Deniz Beser, Joe Cecil, Marjorie Freedman, Jacob Lichtefeld, Mitch Marcus, Sarah Payne, Charles Yang
We introduce and implement a cognitively plausible model for learning from generic language, statements that express generalizations about members of a category and are an important aspect of concept development in language acquisition (Carlson & Pelletier, 1995; Gelman, 2009).
1 code implementation • 5 May 2021 • Ryan Gabbard, Deniz Beser, Jacob Lichtefeld, Joe Cecil, Mitch Marcus, Sarah Payne, Charles Yang, Marjorie Freedman
We present ADAM, a software system for designing and running child language learning experiments in Python.
no code implementations • ACL 2020 • Hongzhi Xu, Jordan Kodner, Mitchell Marcus, Charles Yang
This paper describes a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphological typology.
1 code implementation • 11 Feb 2020 • Mahmoud Elzouka, Charles Yang, Adrian Albert, Sean Lubner, Ravi S. Prasher
We then use a combination of decision tree and random forest models to solve both the forward problem (particle design in, optical properties out) and inverse problem (desired optical properties in, range of particle designs out).
Optics
no code implementations • WS 2019 • Yohei Oseki, Charles Yang, Alec Marantz
Sentences are represented as hierarchical syntactic structures, which have been successfully modeled in sentence processing.
1 code implementation • COLING 2018 • Hongzhi Xu, Mitchell Marcus, Charles Yang, Lyle Ungar
This paper describes an unsupervised model for morphological segmentation that exploits the notion of paradigms, which are sets of morphological categories (e. g., suffixes) that can be applied to a homogeneous set of words (e. g., nouns or verbs).