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pro vyhledávání: '"Matero, Matthew"'
Autor:
Dey, Gourab, Ganesan, Adithya V, Lal, Yash Kumar, Shah, Manal, Sinha, Shreyashee, Matero, Matthew, Giorgi, Salvatore, Kulkarni, Vivek, Schwartz, H. Andrew
Social science NLP tasks, such as emotion or humor detection, are required to capture the semantics along with the implicit pragmatics from text, often with limited amounts of training data. Instruction tuning has been shown to improve the many capab
Externí odkaz:
http://arxiv.org/abs/2402.01980
Natural language is generated by people, yet traditional language modeling views words or documents as if generated independently. Here, we propose human language modeling (HuLM), a hierarchical extension to the language modeling problem whereby a hu
Externí odkaz:
http://arxiv.org/abs/2205.05128
Recent works have demonstrated ability to assess aspects of mental health from personal discourse. At the same time, pre-trained contextual word embedding models have grown to dominate much of NLP but little is known empirically on how to best apply
Externí odkaz:
http://arxiv.org/abs/2112.13795
Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens. However, modeling human language at higher-levels of context (i.e., sequ
Externí odkaz:
http://arxiv.org/abs/2109.08113
In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within modern transformer-based language models, limiting th
Externí odkaz:
http://arxiv.org/abs/2105.03484
Akademický článek
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Akademický článek
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Publikováno v:
NPJ Digital Medicine; 3/17/2023, Vol. 6 Issue 1, p1-1, 1p
Autor:
Matero M; Stony Brook University., Schwartz HA; Stony Brook University.
Publikováno v:
Proceedings of COLING. International Conference on Computational Linguistics [Proc Int Conf Comput Ling] 2020 Dec; Vol. 2020, pp. 2913-2923.