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My research focuses on analyzing and improving the neural network’s ability to understand the compositional structures underlying natural language sentences. In the past, I showed how existing models lack compositionality and take reasoning shortcuts. I then designed interpretable and modular models that can answer complex multi-hop questions more robustly and also collected a multi-hop fact verification dataset HoVer to motivate future work. I also incorporated Tensor-Product into a Transformer for better abstractive summarization. My ultimate goal is to build AI systems that can compositionally recombine structures and contents in understanding natural language and comprehending this world.
研究兴趣
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CoRR (2023): 14549-14566
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user-5d4bc4a8530c70a9b361c870(2021)
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57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019) (2019): 2726-2736
2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019): PROCEEDINGS OF THE CONFERENCE (2019): 4474-4484
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