YoungSheldon at SemEval-2021 Task 7: Fine-tuning Is All You Need

Sharma, Mayukh; Kandasamy, Ilanthenral; Vasantha, W. B.

doi:10.18653/v1/2021.semeval-1.161

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Finetuning (Qiu et al, 2020) pre-trained language models has become a popular approach in the deep learning community (Sharma et al, 2021b). It is a form of transfer learning that utilizes models trained on enormous amounts of unannotated text data to learn general-purpose representations.…”

Section: Introductionmentioning

confidence: 99%

R2D2 at SemEval-2022 Task 5: Attention is only as good as its Values! A multimodal system for identifying misogynist memes

Sharma¹,

Kandasamy²,

Vasantha³

2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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This paper describes the multimodal deep learning system proposed for SemEval 2022 Task 5: MAMI -Multimedia Automatic Misogyny Identification. We participated in both Subtasks, i.e. Subtask A: Misogynous meme identification, and Subtask B: Identifying type of misogyny among potential overlapping categories (stereotype, shaming, objectification, violence). The proposed architecture uses pretrained models as feature extractors for text and images. We use these features to learn multimodal representation using methods like concatenation and scaled dot product attention. Classification layers are used on fused features as per the subtask definition. We also performed experiments using unimodal models for setting up comparative baselines. Our best performing system achieved an F1 score of 0.757 and was ranked 3 rd in Subtask A. On Subtask B, our system performed well with an F1 score of 0.690 and was ranked 10 th on the leaderboard. We further show extensive experiments using combinations of different pre-trained models which will be helpful as baselines for future work.

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Section: Introductionmentioning

confidence: 99%

R2D2 at SemEval-2022 Task 5: Attention is only as good as its Values! A multimodal system for identifying misogynist memes

Sharma¹,

Kandasamy²,

Vasantha³

2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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“…This information is used in downstream tasks by simply finetuning task-specific datasets. PLMs have shown remarkable performance on such downstream tasks using the simple finetuning approach (Sharma et al, 2021b). We extend the same idea to identify sarcasm (Subtask A) and identify the type of irony (Subtask B).…”

Section: Introductionmentioning

confidence: 99%

“…These models can be finetuned on various downstream tasks using task-specific datasets. Finetuning allows models to adapt to small task-specific datasets easily and shows promising results (Sharma et al, 2021b). Next, we provide a summary of PLMs used in our approach.…”

Section: Introductionmentioning

confidence: 99%

R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasm

Sharma¹,

Kandasamy²,

Vasantha³

2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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This paper describes our system used for Se-mEval 2022 Task 6: iSarcasmEval: Intended Sarcasm Detection in English and Arabic. We participated in all subtasks based on only English datasets. Pre-trained Language Models (PLMs) have become a de-facto approach for most natural language processing tasks. In our work, we evaluate the performance of these models for identifying sarcasm. For Subtask A and Subtask B, we used simple finetuning on PLMs. For Subtask C, we propose a Siamese network architecture trained using a combination of cross-entropy and distancemaximisation loss. Our model was ranked 7 th in Subtask B, 8 th in Subtask C (English), and performed well in Subtask A (English). In our work, we also present the comparative performance of different PLMs for each Subtask.

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SemEval 2021 Task 7: HaHackathon, Detecting and Rating Humor and Offense

Meaney¹,

Wilson²,

Chiruzzo³

et al. 2021

Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

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SemEval 2021 Task 7, HaHackathon, was the first shared task to combine the previously separate domains of humor detection and offense detection. We collected 10,000 texts from Twitter and the Kaggle Short Jokes dataset, and had each annotated for humor and offense by 20 annotators aged 18-70. Our subtasks were binary humor detection, prediction of humor and offense ratings, and a novel controversy task: to predict if the variance in the humor ratings was higher than a specific threshold. The subtasks attracted 36-58 submissions, with most of the participants choosing to use pre-trained language models. Many of the highest performing teams also implemented additional optimization techniques, including task-adaptive training and adversarial training. The results suggest that the participating systems are well suited to humor detection, but that humor controversy is a more challenging task. We discuss which models excel in this task, which auxiliary techniques boost their performance, and analyze the errors which were not captured by the best systems.

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YoungSheldon at SemEval-2021 Task 7: Fine-tuning Is All You Need

Cited by 3 publications

References 17 publications

R2D2 at SemEval-2022 Task 5: Attention is only as good as its Values! A multimodal system for identifying misogynist memes

R2D2 at SemEval-2022 Task 5: Attention is only as good as its Values! A multimodal system for identifying misogynist memes

R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasm

SemEval 2021 Task 7: HaHackathon, Detecting and Rating Humor and Offense

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