Sean Benhur


Transformers at SemEval-2022 Task 5: A Feature Extraction based Approach for Misogynous Meme Detection
Shankar Mahadevan | Sean Benhur | Roshan Nayak | Malliga Subramanian | Kogilavani Shanmugavadivel | Kanchana Sivanraju | Bharathi Raja Chakravarthi
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Social media is an idea created to make theworld smaller and more connected. Recently,it has become a hub of fake news and sexistmemes that target women. Social Media shouldensure proper women’s safety and equality. Filteringsuch information from social media is ofparamount importance to achieving this goal.In this paper, we describe the system developedby our team for SemEval-2022 Task 5: MultimediaAutomatic Misogyny Identification. Wepropose a multimodal training methodologythat achieves good performance on both thesubtasks, ranking 4th for Subtask A (0.718macro F1-score) and 9th for Subtask B (0.695macro F1-score) while exceeding the baselineresults by good margins.

Span Extraction Aided Improved Code-mixed Sentiment Classification
Ramaneswaran S | Sean Benhur | Sreyan Ghosh
Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)

Sentiment classification is a fundamental NLP task of detecting the sentiment polarity of a given text. In this paper we show how solving sentiment span extraction as an auxiliary task can help improve final sentiment classification performance in a low-resource code-mixed setup. To be precise, we don’t solve a simple multi-task learning objective, but rather design a unified transformer framework that exploits the bidirectional connection between the two tasks simultaneously. To facilitate research in this direction we release gold-standard human-annotated sentiment span extraction dataset for Tamil-english code-switched texts. Extensive experiments and strong baselines show that our proposed approach outperforms sentiment and span prediction by 1.27% and 2.78% respectively when compared to the best performing MTL baseline. We also establish the generalizability of our approach on the Twitter Sentiment Extraction dataset. We make our code and data publicly available on GitHub

DE-ABUSE@TamilNLP-ACL 2022: Transliteration as Data Augmentation for Abuse Detection in Tamil
Vasanth Palanikumar | Sean Benhur | Adeep Hande | Bharathi Raja Chakravarthi
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages

With the rise of social media and internet, thereis a necessity to provide an inclusive space andprevent the abusive topics against any gender,race or community. This paper describes thesystem submitted to the ACL-2022 shared taskon fine-grained abuse detection in Tamil. In ourapproach we transliterated code-mixed datasetas an augmentation technique to increase thesize of the data. Using this method we wereable to rank 3rd on the task with a 0.290 macroaverage F1 score and a 0.590 weighted F1score

Findings of the Shared Task on Emotion Analysis in Tamil
Anbukkarasi Sampath | Thenmozhi Durairaj | Bharathi Raja Chakravarthi | Ruba Priyadharshini | Subalalitha Cn | Kogilavani Shanmugavadivel | Sajeetha Thavareesan | Sathiyaraj Thangasamy | Parameswari Krishnamurthy | Adeep Hande | Sean Benhur | Kishore Ponnusamy | Santhiya Pandiyan
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages

This paper presents the overview of the shared task on emotional analysis in Tamil. The result of the shared task is presented at the workshop. This paper presents the dataset used in the shared task, task description, and the methodology used by the participants and the evaluation results of the submission. This task is organized as two Tasks. Task A is carried with 11 emotions annotated data for social media comments in Tamil and Task B is organized with 31 fine-grained emotion annotated data for social media comments in Tamil. For conducting experiments, training and development datasets were provided to the participants and results are evaluated for the unseen data. Totally we have received around 24 submissions from 13 teams. For evaluating the models, Precision, Recall, micro average metrics are used.


Hypers at ComMA@ICON: Modelling Aggressive, Gender Bias and Communal Bias Identification
Sean Benhur | Roshan Nayak | Kanchana Sivanraju | Adeep Hande | Cn Subalalitha | Ruba Priyadharshini | Bharathi Raja Chakravarthi
Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification

Due to the exponential increasing reach of social media, it is essential to focus on its negative aspects as it can potentially divide society and incite people into violence. In this paper, we present our system description of work on the shared task ComMA@ICON, where we have to classify how aggressive the sentence is and if the sentence is gender-biased or communal biased. These three could be the primary reasons to cause significant problems in society. Our approach utilizes different pretrained models with Attention and mean pooling methods. We were able to get Rank 1 with 0.253 Instance F1 score on Bengali, Rank 2 with 0.323 Instance F1 score on multilingual set, Rank 4 with 0.129 Instance F1 score on meitei and Rank 5 with 0.336 Instance F1 score on Hindi. The source code and the pretrained models of this work can be found here.