![]() LT3 at SemEval-2020 Task 7: Comparing Feature-Based and Transformer-Based Approaches to Detect Funny Headlines The latter system was also our final submission to the competition and is ranked seventh among the 49 participating teams, with a root-mean-square error (RMSE) of 0.5253.", The second system is a deep learning-based approach that uses the pre-trained language model RoBERTa to learn latent features in the news headlines that are useful to predict the funniness of each headline. word embeddings, string similarity, part-of-speech tags, perplexity scores, named entity recognition) in a Nu Support Vector Regressor (NuSVR). Our first system is a feature-based machine learning system that combines different types of information (e.g. Publisher = "International Committee for Computational Linguistics",Ībstract = "This paper presents two different systems for the SemEval shared task 7 on Assessing Humor in Edited News Headlines, sub-task 1, where the aim was to estimate the intensity of humor generated in edited headlines. Cite (Informal): LT3 at SemEval-2020 Task 7: Comparing Feature-Based and Transformer-Based Approaches to Detect Funny Headlines (Vanroy et al., SemEval 2020) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Data = "val-2020 Task 7: Comparing Feature-Based and Transformer-Based Approaches to Detect Funny Headlines",īooktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation", International Committee for Computational Linguistics. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1033–1040, Barcelona (online). LT3 at SemEval-2020 Task 7: Comparing Feature-Based and Transformer-Based Approaches to Detect Funny Headlines. SIGSEM Publisher: International Committee for Computational Linguistics Note: Pages: 1033–1040 Language: URL: DOI: 10.18653/v1/meval-1.135 Bibkey: vanroy-etal-2020-lt3 Cite (ACL): Bram Vanroy, Sofie Labat, Olha Kaminska, Els Lefever, and Veronique Hoste. Anthology ID: meval-1.135 Volume: Proceedings of the Fourteenth Workshop on Semantic Evaluation Month: December Year: 2020 Address: Barcelona (online) Venue: SemEval SIGs: SIGLEX The latter system was also our final submission to the competition and is ranked seventh among the 49 participating teams, with a root-mean-square error (RMSE) of 0.5253. Abstract This paper presents two different systems for the SemEval shared task 7 on Assessing Humor in Edited News Headlines, sub-task 1, where the aim was to estimate the intensity of humor generated in edited headlines.
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