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pro vyhledávání: '"Xu Xinnuo"'
Despite the increasing effectiveness of language models, their reasoning capabilities remain underdeveloped. In particular, causal reasoning through counterfactual question answering is lacking. This work aims to bridge this gap. We first derive nove
Externí odkaz:
http://arxiv.org/abs/2410.03767
Autor:
Pham, Hai X., Hadji, Isma, Xu, Xinnuo, Degutyte, Ziedune, Rainey, Jay, Kazakos, Evangelos, Fazly, Afsaneh, Tzimiropoulos, Georgios, Martinez, Brais
In this paper, we focus on task-specific question answering (QA). To this end, we introduce a method for generating exhaustive and high-quality training data, which allows us to train compact (e.g., run on a mobile device), task-specific QA models th
Externí odkaz:
http://arxiv.org/abs/2401.13594
Publikováno v:
Findings of EMNLP 2023
Data-to-text generation involves transforming structured data, often represented as predicate-argument tuples, into coherent textual descriptions. Despite recent advances, systems still struggle when confronted with unseen combinations of predicates,
Externí odkaz:
http://arxiv.org/abs/2312.02748
Publikováno v:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing Findings (EMNLP2021 Findings)
One of the most challenging aspects of current single-document news summarization is that the summary often contains 'extrinsic hallucinations', i.e., facts that are not present in the source document, which are often derived via world knowledge. Thi
Externí odkaz:
http://arxiv.org/abs/2109.10650
Publikováno v:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL2021)
Natural Language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language. For large-scale conversational systems, where it is common to have over hundre
Externí odkaz:
http://arxiv.org/abs/2106.05589
Publikováno v:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL2021)
We present AGGGEN (pronounced 'again'), a data-to-text model which re-introduces two explicit sentence planning stages into neural data-to-text systems: input ordering and input aggregation. In contrast to previous work using sentence planning, our m
Externí odkaz:
http://arxiv.org/abs/2106.05580
Publikováno v:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3981-3991, Brussels, Belgium, November 2018
We present three enhancements to existing encoder-decoder models for open-domain conversational agents, aimed at effectively modeling coherence and promoting output diversity: (1) We introduce a measure of coherence as the GloVe embedding similarity
Externí odkaz:
http://arxiv.org/abs/1809.06873
Autor:
Papaioannou, Ioannis, Curry, Amanda Cercas, Part, Jose L., Shalyminov, Igor, Xu, Xinnuo, Yu, Yanchao, Dušek, Ondřej, Rieser, Verena, Lemon, Oliver
Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence. This year, the Amazon Alexa Prize challenge was announced for the first time, where real customers get to rate systems developed by leading universities worldwi
Externí odkaz:
http://arxiv.org/abs/1712.07558
Akademický článek
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Autor:
Ioannis Papaioannou, Cercas Curry, Amanda, Jose Part, Igor Shalyminov, Xu Xinnuo, Yanchao Yu, Ondrej Dusek, Verena Rieser, Oliver Lemon
Publikováno v:
Heriot-Watt University
Ondrej Dusek
ResearcherID
Ondrej Dusek
ResearcherID
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::130a19d0b8165fd73539e892697a0a9f
https://researchportal.hw.ac.uk/en/publications/9c1691bb-e103-4ca9-a569-29b16afa3798
https://researchportal.hw.ac.uk/en/publications/9c1691bb-e103-4ca9-a569-29b16afa3798