UMUTeam@LT-EDI-ACL2022: Detecting homophobic and transphobic comments in Tamil
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%).
Code (0)
등록된 구현이 없습니다.
Tasks
SentenceSentence EmbeddingsTransliterationSimilar Papers 제목 키워드 기반
IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media comments
The increased expansion of abusive content on social media platforms negatively affects online users. Transphobic/homophobic content indicates hatred comments for lesbian, gay, transgender, or bisexual people. It leads t…
DiversityZero-Shot LearningDetection of Homophobia & Transphobia in Dravidian Languages: Exploring Deep Learning Methods
The increase in abusive content on online social media platforms is impacting the social life of online users. Use of offensive and hate speech has been making so-cial media toxic. Homophobia and transphobia constitute o…
cantnlp@LT-EDI-2023: Homophobia/Transphobia Detection in Social Media Comments using Spatio-Temporally Retrained Language Models
This paper describes our multiclass classification system developed as part of the LTEDI@RANLP-2023 shared task. We used a BERT-based language model to detect homophobic and transphobic content in social media comments a…
ClassificationLanguage ModelingLanguage ModellingUMUTeam@TamilNLP-ACL2022: Abusive Detection in Tamil using Linguistic Features and Transformers
Social media has become a dangerous place as bullies take advantage of the anonymity the Internet provides to target and intimidate vulnerable individuals and groups. In the past few years, the research community has foc…
PositionSentenceSentence EmbeddingsDataset for Identification of Homophobia and Transophobia in Multilingual YouTube Comments
The increased proliferation of abusive content on social media platforms has a negative impact on online users. The dread, dislike, discomfort, or mistrust of lesbian, gay, transgender or bisexual persons is defined as h…