Zobrazeno 1 - 10
of 263
pro vyhledávání: '"Iryna Gurevych"'
Autor:
Georgia Chalvatzaki, Ali Younes, Daljeet Nandha, An Thai Le, Leonardo F. R. Ribeiro, Iryna Gurevych
Publikováno v:
Frontiers in Robotics and AI, Vol 10 (2023)
Long-horizon task planning is essential for the development of intelligent assistive and service robots. In this work, we investigate the applicability of a smaller class of large language models (LLMs), specifically GPT-2, in robotic task planning b
Externí odkaz:
https://doaj.org/article/4fcfd322123f48e8bfb5e0661410de4c
Publikováno v:
Computational Linguistics, Vol 49, Iss 1 (2023)
Annotated data is an essential ingredient in natural language processing for training and evaluating machine learning models. It is therefore very desirable for the annotations to be of high quality. Recent work, however, has shown that several popul
Externí odkaz:
https://doaj.org/article/c2208525153f468a9148bd1a812b3032
Publikováno v:
Computational Linguistics, Vol 47, Iss 3, Pp 575-614 (2021)
Cross-document event coreference resolution (CDCR) is an NLP task in which mentions of events need to be identified and clustered throughout a collection of documents. CDCR aims to benefit downstream multidocument applications, but despite recent pro
Externí odkaz:
https://doaj.org/article/5006903210e940a0be54c2de53fb57ab
Publikováno v:
Computational Linguistics, Vol 48, Iss 4 (2022)
The authors of this work (“Annotation Curricula to Implicitly Train Non-Expert Annotators” by Ji-Ung Lee, Jan-Christoph Klie, and Iryna Gurevych in Computational Linguistics 48:2 https://doi.org/10.1162/coli_a_00436) discovered an incorrect inequ
Externí odkaz:
https://doaj.org/article/c0ba1a2094d947249336e959488c3bc2
Publikováno v:
Computational Linguistics, Vol 48, Iss 4 (2022)
Peer review is a key component of the publishing process in most fields of science. Increasing submission rates put a strain on reviewing quality and efficiency, motivating the development of applications to support the reviewing and editorial work.
Externí odkaz:
https://doaj.org/article/253988e94b0746eda984ede06d09c3cb
Publikováno v:
Computational Linguistics, Vol 48, Iss 2 (2022)
Annotation studies often require annotators to familiarize themselves with the task, its annotation scheme, and the data domain. This can be overwhelming in the beginning, mentally taxing, and induce errors into the resulting annotations; especially
Externí odkaz:
https://doaj.org/article/5b25a81bccc24a7e917d571292f34883
Autor:
Christian Stab, Iryna Gurevych
Publikováno v:
Computational Linguistics, Vol 43, Iss 3 (2017)
In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint model for detecting argumentation structures. The proposed model globa
Externí odkaz:
https://doaj.org/article/09cf7793add743149d67ed71c6951456
Autor:
Ivan Habernal, Iryna Gurevych
Publikováno v:
Computational Linguistics, Vol 43, Iss 1 (2016)
The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual W
Externí odkaz:
https://doaj.org/article/8d1c317c6e6f496c9d7992f078d72f3e
Publikováno v:
Communication Methods and Measures. 17:150-184
Publikováno v:
Journal of Service Research. 25:537-548
If service providers can identify reasons users are in favor of or against a service, they have insightful information that can help them understand user behavior and what they need to do to change such behavior. This article argues that the novel te