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My research interest is to uncover structural, or other linguistic biases exist in natural language, and explore the interaction between such insights into language and natural language processing applications.
While end-to-end approaches get much popularity in recent NLP, it seems unlikely that all aspects of language can be learned in a completely data-driven manner. My current interest is to investigate which aspects of language can or cannot be acquired from the data alone, to know how we can make NLP systems more robust, and ultimately humanlike.
I’m also interested in formalisms of syntactic and semantic representations, as well as an efficient algorithm for a particular model, in particular involving structured prediction. Examples of this work include our ACL 2017, EMNLP 2016, and ACL 2015 papers.
My dissertation was about finding a syntactic principle, universal across languages, and applying it to unsupervised grammar induction (unsupervised parsing).
While end-to-end approaches get much popularity in recent NLP, it seems unlikely that all aspects of language can be learned in a completely data-driven manner. My current interest is to investigate which aspects of language can or cannot be acquired from the data alone, to know how we can make NLP systems more robust, and ultimately humanlike.
I’m also interested in formalisms of syntactic and semantic representations, as well as an efficient algorithm for a particular model, in particular involving structured prediction. Examples of this work include our ACL 2017, EMNLP 2016, and ACL 2015 papers.
My dissertation was about finding a syntactic principle, universal across languages, and applying it to unsupervised grammar induction (unsupervised parsing).
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EACL (Findings)pp.2255-2260, (2023)
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PROCEEDINGS OF THE SIXTH WORKSHOP ON STRUCTURED PREDICTION FOR NLP (SPNLP 2022) (2022): 1-10
Journal of Natural Language Processingno. 4 (2021): 938-964
58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020) (2020)
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