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pro vyhledávání: '"KÜBLER, SANDRA"'
Autor:
Wang, Haining, Clark, Jason, McKelvey, Hannah, Sterman, Leila, Gao, Zheng, Tian, Zuoyu, Kübler, Sandra, Liu, Xiaozhong
A vast amount of scholarly work is published daily, yet much of it remains inaccessible to the general public due to dense jargon and complex language. To address this challenge in science communication, we introduce a reinforcement learning framewor
Externí odkaz:
http://arxiv.org/abs/2410.17088
Despite the tremendous recent progress on natural language inference (NLI), driven largely by large-scale investment in new datasets (e.g., SNLI, MNLI) and advances in modeling, most progress has been limited to English due to a lack of reliable data
Externí odkaz:
http://arxiv.org/abs/2010.05444
Akademický článek
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We present a new logic-based inference engine for natural language inference (NLI) called MonaLog, which is based on natural logic and the monotonicity calculus. In contrast to existing logic-based approaches, our system is intentionally designed to
Externí odkaz:
http://arxiv.org/abs/1910.08772
This paper describes the UM-IU@LING's system for the SemEval 2019 Task 6: OffensEval. We take a mixed approach to identify and categorize hate speech in social media. In subtask A, we fine-tuned a BERT based classifier to detect abusive content in tw
Externí odkaz:
http://arxiv.org/abs/1904.03450
Akademický článek
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Autor:
Kirov, Christo, Cotterell, Ryan, Sylak-Glassman, John, Walther, Géraldine, Vylomova, Ekaterina, Xia, Patrick, Faruqui, Manaal, Mielke, Sabrina J., McCarthy, Arya D., Kübler, Sandra, Yarowsky, David, Eisner, Jason, Hulden, Mans
The Universal Morphology UniMorph project is a collaborative effort to improve how NLP handles complex morphology across the world's languages. The project releases annotated morphological data using a universal tagset, the UniMorph schema. Each infl
Externí odkaz:
http://arxiv.org/abs/1810.11101
We present a machine learning approach to distinguish texts translated to Chinese (by humans) from texts originally written in Chinese, with a focus on a wide range of syntactic features. Using Support Vector Machines (SVMs) as classifier on a genre-
Externí odkaz:
http://arxiv.org/abs/1804.08756