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My fundamental research interest is language learning as systematic generalization. Humans are able to generate unseen novel utterances from a limited sample of data, while current machine learning approaches fall short on. Building an intelligent agent that is able to acquire the language as efficient as humans is the important next step as we are seeing the marginal effect of increasing model size of language models. I believe there are two main missing pieces of puzzles:
Humans learn the language in an embodied environment, and humans acquire language as a tool to influence the world around them. We should model situated language learning beyond learning from a static corpus.
Language evolves and adapts to an iterated transmission process so that it becomes structured and easy-to-acquire for the later generations. We should model this cultural evolution aspect of language in our language learning.
Humans learn the language in an embodied environment, and humans acquire language as a tool to influence the world around them. We should model situated language learning beyond learning from a static corpus.
Language evolves and adapts to an iterated transmission process so that it becomes structured and easy-to-acquire for the later generations. We should model this cultural evolution aspect of language in our language learning.
Research Interests
Papers共 23 篇Author StatisticsCo-AuthorSimilar Experts
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NEURAL INFORMATION PROCESSING, ICONIP 2023, PT II (2024): 413-424
Yufei Cui,Ziquan Liu, Yixin Chen,Yuchen Lu, Xinyue Yu, Xue (Steve) Liu,Tei-Wei Kuo,Miguel Rodrigues,Chun Jason Xue,Antoni B. Chan
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ICLR 2023 (2023)
arXiv (Cornell University) (2022)
CoRR (2022)
International Conference on Machine Learningpp.24631-24645, (2022)
CoRR (2022)
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