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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 "We will Reduce Taxes" - Identifying Election Pledges with Language Models ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Adhikari, pankaj.adhikari@monash.edu; Fornaciari, Tommaso. - : Underline Science Inc., 2021 BASE Show details 2 SemEval 2021 Task 12: Learning with Disagreement ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Chamberlain, Jon; Dumitrache, Anca. - : Underline Science Inc., 2021 BASE Show details 3 SemEval-2021 Task 12: Learning with Disagreements Uma, Alexandra; Fornaciari, Tommaso; Dumitrache, Anca; Miller, Tristan; Chamberlain, Jon; Plank, Barbara; Simpson, Edwin; Poesio, Massimo. - : Association for Computational Linguistics, 2021 Abstract: Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assume that a single preferred interpretation exists for each item, which is at best an idealization. The aim of the SemEval-2021 shared task on learning with disagreements (Le-Wi-Di) was to provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images. In this paper we describe the shared task and its results. URL: http://repository.essex.ac.uk/31851/1/2021.semeval-1.41.pdf http://repository.essex.ac.uk/31851/ https://doi.org/10.18653/v1/2021.semeval-1.41 BASE Hide details 4 We Need to Consider Disagreement in Evaluation Basile, Valerio; Fell, Michael; Fornaciari, Tommaso. - : Association for Computational Linguistics, 2021. : country:USA, 2021. : place:Stroudsburg, PA, 2021 BASE Show details 5 Fake opinion detection: how similar are crowdsourced datasets to real data? [<Journal>] Fornaciari, Tommaso [Verfasser]; Cagnina, Leticia [Verfasser]; Rosso, Paolo [Verfasser]. DNB Subject Category Language Show details 6 Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data? Fornaciari, Tommaso; Cagnina, Leticia; Rosso, Paolo. - : Springer-Verlag, 2020 BASE Show details 7 Identifying fake Amazon reviews as learning from crowds Fornaciari, Tommaso; Poesio, Massimo. - : Association for Computational Linguistics, 2014 BASE Show details

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