Search Results for author: Michael C. Frank

Found 12 papers, 7 papers with code

The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences

no code implementations14 Jun 2024 Bria Long, Violet Xiang, Stefan Stojanov, Robert Z. Sparks, Zi Yin, Grace E. Keene, Alvin W. M. Tan, Steven Y. Feng, Chengxu Zhuang, Virginia A. Marchman, Daniel L. K. Yamins, Michael C. Frank

Egocentric video capturing children's experience -- their ''training data'' -- is a key ingredient for comparison of humans and models and for the development of algorithmic innovations to bridge this gap.

Depth Estimation Image Segmentation +5

DevBench: A multimodal developmental benchmark for language learning

no code implementations14 Jun 2024 Alvin Wei Ming Tan, Sunny Yu, Bria Long, Wanjing Anya Ma, Tonya Murray, Rebecca D. Silverman, Jason D. Yeatman, Michael C. Frank

Across tasks, models exhibit variation in their closeness to human response patterns, and models that perform better on a task also more closely resemble human behavioral responses.

Auxiliary task demands mask the capabilities of smaller language models

1 code implementation3 Apr 2024 Jennifer Hu, Michael C. Frank

Developmental psychologists have argued about when cognitive capacities such as language understanding or theory of mind emerge.

Learning the meanings of function words from grounded language using a visual question answering model

1 code implementation16 Aug 2023 Eva Portelance, Michael C. Frank, Dan Jurafsky

Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning, as well as early evidence that they are sensitive to alternative expressions when interpreting language.

Logical Reasoning Question Answering +2

The Emergence of the Shape Bias Results from Communicative Efficiency

1 code implementation CoNLL (EMNLP) 2021 Eva Portelance, Michael C. Frank, Dan Jurafsky, Alessandro Sordoni, Romain Laroche

By the age of two, children tend to assume that new word categories are based on objects' shape, rather than their color or texture; this assumption is called the shape bias.

From partners to populations: A hierarchical Bayesian account of coordination and convention

1 code implementation12 Apr 2021 Robert D. Hawkins, Michael Franke, Michael C. Frank, Adele E. Goldberg, Kenny Smith, Thomas L. Griffiths, Noah D. Goodman

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.

Continual Learning

Characterizing the dynamics of learning in repeated reference games

1 code implementation16 Dec 2019 Robert D. Hawkins, Michael C. Frank, Noah D. Goodman

The language we use over the course of conversation changes as we establish common ground and learn what our partner finds meaningful.

Pedagogical learning

no code implementations26 Nov 2017 Long Ouyang, Michael C. Frank

samples are, in a sense, uninformative---they produce data without regard to how good this data is for learning.

BIG-bench Machine Learning Clustering

Prosodic Features from Large Corpora of Child-Directed Speech as Predictors of the Age of Acquisition of Words

1 code implementation27 Sep 2017 Lea Frermann, Michael C. Frank

The impressive ability of children to acquire language is a widely studied phenomenon, and the factors influencing the pace and patterns of word learning remains a subject of active research.

Parsing entire discourses as very long strings: Capturing topic continuity in grounded language learning

no code implementations TACL 2013 Minh-Thang Luong, Michael C. Frank, Mark Johnson

Grounded language learning, the task of mapping from natural language to a representation of meaning, has attracted more and more interest in recent years.

Grounded language learning Sentence

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