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Rodrigo Agerri

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Articles by Rodrigo Agerri

Computer Science > Computation and Language

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By Maite Oronoz, Rodrigo Agerri
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2503.05328] Dynamic Knowledge Integration for Evidence-Driven Counter-Argument Generation with Large Language Models

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By Maite Oronoz, Rodrigo Agerri
| arXiv
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Critical Questions Generation: Motivation and Challenges

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By Rodrigo Agerri, Blanca Calvo
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques

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By Maite Oronoz, Rodrigo Agerri
| arXiv
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[2406.07964] Political Leaning Inference through Plurinational Scenarios

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By Rodrigo Agerri, de Landa, Joseba Fernandez
| arXiv
[Submitted on 12 Jun 2024] Title:Political Leaning Inference through Plurinational Scenarios View a PDF of the paper titled Political Leaning Inference through Plurinational Scenarios, by Joseba Fernandez de Landa and Rodrigo Agerri View PDF HTML (experimental) Abstract:Social media users express their political preferences via interaction with other users, by spontaneous declarations or by participation in communities within the network.
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GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction

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By de Lacalle, Oier Lopez, Iker García-Ferrero, Rodrigo Agerri
| arXiv
arXiv:2310.03668 (cs) [Submitted on 5 Oct 2023 (v1), last revised 6 Mar 2024 (this version, v5)] Download PDF HTML (experimental) Comments: Subjects: Computation and Language (cs.CL) Cite as: arXiv:2310.03668 [cs.CL] (or arXiv:2310.03668v5 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2310.03668 Submission history From: Iker García-Ferrero [ view email] [v1] Thu, 5 Oct 2023 16:43:13 UTC (9,011 KB) [v2] Fri, 6 Oct 2023 17:41:15 UTC (9,011 KB) [v3] Mon, 11 Dec 2023 08:24:40 UTC...
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[2301.10527] Cross-lingual Argument Mining in the Medical Domain

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By Rodrigo Agerri
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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