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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 – 2 of 2 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; Hovy, Dirk; Naurin, Elin; Runeson, Julia; Thomson, Robert. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.301 Abstract: In an election campaign, political parties pledge to implement various projects--should they be elected. But do they follow through? To track election pledges from parties' election manifestos, we need to distinguish between pledges and general statements. In this paper, we use election manifestos of Swedish and Indian political parties to learn neural models that distinguish actual pledges from generic political positions. Since pledges might vary by election year and party, we implement a Multi-Task Learning (MTL) setup, predicting election year and manifesto's party as auxiliary tasks. Pledges can also span several sentences, so we use hierarchical models that incorporate contextual information. Lastly, we evaluate the models in a Zero-Shot Learning (ZSL) framework across countries and languages. Our results indicate that year and party have predictive power even in ZSL, while context introduces some noise. We finally discuss the ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/26392-'we-will-reduce-taxes'---identifying-election-pledges-with-language-models https://dx.doi.org/10.48448/x2b1-ym56 BASE Hide 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

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