Zobrazeno 1 - 10
of 1 637
pro vyhledávání: '"A. Scarton"'
Autor:
He, Wei, Vieira, Tiago Kramer, Garcia, Marcos, Scarton, Carolina, Idiart, Marco, Villavicencio, Aline
Idiomatic expressions are an integral part of human languages, often used to express complex ideas in compressed or conventional ways (e.g. eager beaver as a keen and enthusiastic person). However, their interpretations may not be straightforwardly l
Externí odkaz:
http://arxiv.org/abs/2411.02610
Autor:
Srba, Ivan, Razuvayevskaya, Olesya, Leite, João A., Moro, Robert, Schlicht, Ipek Baris, Tonelli, Sara, García, Francisco Moreno, Lottmann, Santiago Barrio, Teyssou, Denis, Porcellini, Valentin, Scarton, Carolina, Bontcheva, Kalina, Bielikova, Maria
In the current era of social media and generative AI, an ability to automatically assess the credibility of online social media content is of tremendous importance. Credibility assessment is fundamentally based on aggregating credibility signals, whi
Externí odkaz:
http://arxiv.org/abs/2410.21360
In-context learning (ICL) performance is known to be sensitive to the prompt design, yet the impact of class label options in zero-shot classification has been largely overlooked. This study presents the first comprehensive empirical study investigat
Externí odkaz:
http://arxiv.org/abs/2410.19195
Overview of the BioLaySumm 2024 Shared Task on the Lay Summarization of Biomedical Research Articles
This paper presents the setup and results of the second edition of the BioLaySumm shared task on the Lay Summarisation of Biomedical Research Articles, hosted at the BioNLP Workshop at ACL 2024. In this task edition, we aim to build on the first edit
Externí odkaz:
http://arxiv.org/abs/2408.08566
Incorporating extra-textual context such as film metadata into the machine translation (MT) pipeline can enhance translation quality, as indicated by automatic evaluation in recent work. However, the positive impact of such systems in industry remain
Externí odkaz:
http://arxiv.org/abs/2407.00108
Publikováno v:
Findings of the Association for Computational Linguistics. ACL 2024. 12473-12485 (2024)
Accurately modeling idiomatic or non-compositional language has been a longstanding challenge in Natural Language Processing (NLP). This is partly because these expressions do not derive their meanings solely from their constituent words, but also du
Externí odkaz:
http://arxiv.org/abs/2406.15175
This work introduces EUvsDisinfo, a multilingual dataset of disinformation articles originating from pro-Kremlin outlets, along with trustworthy articles from credible / less biased sources. It is sourced directly from the debunk articles written by
Externí odkaz:
http://arxiv.org/abs/2406.12614
Lay summarisation aims to produce summaries of scientific articles that are comprehensible to non-expert audiences. However, previous work assumes a one-size-fits-all approach, where the content and style of the produced summary are entirely dependen
Externí odkaz:
http://arxiv.org/abs/2406.05625
Autor:
Vasilakes, Jake, Zhao, Zhixue, Vykopal, Ivan, Gregor, Michal, Hyben, Martin, Scarton, Carolina
Addressing online disinformation requires analysing narratives across languages to help fact-checkers and journalists sift through large amounts of data. The ExU project focuses on developing AI-based models for multilingual disinformation analysis,
Externí odkaz:
http://arxiv.org/abs/2406.15443
In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks. Typically, when annotating such tasks annotators are only asked to provide a single label for each sample an
Externí odkaz:
http://arxiv.org/abs/2311.05265