Abstract | ||
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This working-notes are about the participation of the UMUTeam in a LT-EDI shared task concerning the identification of homophobic and transphobic comments in YouTube. These comments are written in English, which has high availability to machine-learning resources; Tamil, which has fewer resources; and a transliteration from Tamil to Roman script combined with English sentences. To carry out this shared task, we train a neural network that combines several feature sets applying a knowledge integration strategy. These features are linguistic features extracted from a tool developed by our research group and contextual and non-contextual sentence embeddings. We ranked 7th for English subtask (macro f1-score of 45%), 3rd for Tamil subtask (macro f1-score of 82%), and 2nd for Tamil-English subtask (macro f1-score of 58%). |
Year | DOI | Venue |
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2022 | 10.18653/v1/2022.ltedi-1.16 | PROCEEDINGS OF THE SECOND WORKSHOP ON LANGUAGE TECHNOLOGY FOR EQUALITY, DIVERSITY AND INCLUSION (LTEDI 2022) |
DocType | Volume | Citations |
Conference | Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
José Antonio García-Díaz | 1 | 3 | 1.74 |
Camilo Caparros-Laiz | 2 | 0 | 0.34 |
Rafael Valencia-García | 3 | 0 | 0.68 |