Zobrazeno 1 - 10
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pro vyhledávání: '"Andrews, Martin"'
Autor:
Andrews, Martin, Witteveen, Sam
Cryptic crossword clues are challenging cognitive tasks, for which new test sets are released on a daily basis by multiple international newspapers. Each cryptic clue contains both the definition of the answer to be placed in the crossword grid (in c
Externí odkaz:
http://arxiv.org/abs/2407.08824
Autor:
Witteveen, Sam, Andrews, Martin
With the spread of the use of Text2Img diffusion models such as DALL-E 2, Imagen, Mid Journey and Stable Diffusion, one challenge that artists face is selecting the right prompts to achieve the desired artistic output. We present techniques for measu
Externí odkaz:
http://arxiv.org/abs/2211.15462
The HadGEM3‐GC3.1 Contribution to the CMIP6 Detection and Attribution Model Intercomparison Project.
Autor:
Jones, Gareth S.1 (AUTHOR) gareth.s.jones@metoffice.gov.uk, Andrews, Martin B.1 (AUTHOR), Andrews, Timothy1 (AUTHOR), Blockley, Ed1 (AUTHOR), Ciavarella, Andrew1 (AUTHOR), Christidis, Nikos1 (AUTHOR), Cotterill, Daniel F.1 (AUTHOR), Lott, Fraser C.1 (AUTHOR), Ridley, Jeff1 (AUTHOR), Stott, Peter A.1 (AUTHOR)
Publikováno v:
Journal of Advances in Modeling Earth Systems. Aug2024, Vol. 16 Issue 8, p1-24. 24p.
Publikováno v:
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
The aim of the CASE 2021 Shared Task 1 (H\"urriyeto\u{g}lu et al., 2021) was to detect and classify socio-political and crisis event information at document, sentence, cross-sentence, and token levels in a multilingual setting, with each of these sub
Externí odkaz:
http://arxiv.org/abs/2110.15599
Creating explanations for answers to science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. This year, to refocus the Textgraphs Shared Task on the problem of gathering relevant statements (rathe
Externí odkaz:
http://arxiv.org/abs/2107.13031
Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. To counter the limitations of methods that view each query-document pair in isolation, we propose the LST
Externí odkaz:
http://arxiv.org/abs/2012.14164
Autor:
Witteveen, Sam, Andrews, Martin
Recently, large language models such as GPT-2 have shown themselves to be extremely adept at text generation and have also been able to achieve high-quality results in many downstream NLP tasks such as text classification, sentiment analysis and ques
Externí odkaz:
http://arxiv.org/abs/1911.09661
The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions. Red Dragon AI's entries used the language of the questions and explanation text directl
Externí odkaz:
http://arxiv.org/abs/1911.08976
Autor:
Andrews, Martin, Witteveen, Sam
The recent (2019-02) demonstration of the power of huge language models such as GPT-2 to memorise the answers to factoid questions raises questions about the extent to which knowledge is being embedded directly within these large models. This short p
Externí odkaz:
http://arxiv.org/abs/1911.08340
Scene graph representations, which form a graph of visual object nodes together with their attributes and relations, have proved useful across a variety of vision and language applications. Recent work in the area has used Natural Language Processing
Externí odkaz:
http://arxiv.org/abs/1909.06273