3218IR at SemEval-2020 Task 11: Conv1D and Word Embedding in Propaganda Span Identification at News Articles

Dimas Sony Dewantara, Indra Budi, Muhammad Okky Ibrohim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In this paper, we present the result of our experiment with a variant of 1 Dimensional Convolutional Neural Network (Conv1D) hyper-parameters value. We describe the system entered by the team of Information Retrieval Lab. Universitas Indonesia (3218IR) in the SemEval 2020 Task 11 Sub Task 1 about propaganda span identification in news articles. The best model obtained an F1 score of 0.24 in the development set and 0.23 in the test set. We show that there is a potential for performance improvement through the use of models with appropriate hyper-parameters. Our system uses a combination of Conv1D and GloVe as Word Embedding to detect propaganda in the fragment text level.

Original languageEnglish
Title of host publication14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings
EditorsAurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
PublisherInternational Committee for Computational Linguistics
Pages1716-1721
Number of pages6
ISBN (Electronic)9781952148316
Publication statusPublished - 2020
Event14th International Workshops on Semantic Evaluation, SemEval 2020 - Barcelona, Spain
Duration: 12 Dec 202013 Dec 2020

Publication series

Name14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings

Conference

Conference14th International Workshops on Semantic Evaluation, SemEval 2020
Country/TerritorySpain
CityBarcelona
Period12/12/2013/12/20

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