TeamOtter at SemEval-2022 Task 5: Detecting Misogynistic Content in Multimodal Memes

Maheshwari, Paridhi; Nangi, Sharmila Reddy

doi:10.18653/v1/2022.semeval-1.88

Cited by 4 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 10 Continued from previous page Leaderboard Sub-task B Team Name Weighted-average F 1 -score 13 TeamOtter* (Maheshwari and Nangi, 2022) 0.680 14 Tathagata Raha* (Raha et al, 2022) 0.679 15 UPB* (Paraschiv et al, 2022) 0…”

Section: C2 Leader-board Of Sub-task Bmentioning

confidence: 99%

“…Leaderboard Sub-task A (Srivastava, 2022) 0.759 R2D2* (Sharma et al, 2022b) 0.757 PAIC (ZHI et al, 2022) 0.755 ymf924 0.755 RubCSG* (Yu et al, 2022) 0.755 hate-alert 0.753 AMS_ADRN* (Li et al, 2022) 0.746 TIB-VA* (Hakimov et al, 2022) 0.734 4 union 0.727 Unibo* (Muti et al, 2022) 0.727 MMVAE* (Gu et al, 2022b) 0.723 YMAI* (Habash et al, 2022) 0.722 Transformers* (Mahadevan et al, 2022) 0.718 taochen* (Tao and jae Kim, 2022) 0.716 codec* (Mahran et al, 2022) 0.715 QMUL* 0.714 UPB* (Paraschiv et al, 2022) 0.714 HateU* (Arango et al, 2022) 0.712 yuanyuanya 0.708 Triplo7* (Attanasio et al, 2022) 0.699 InfUfrgs* (Lorentz and Moreira, 2022) 0.698 Mitra Behzadi* (Behzadi et al, 2022) 0.694 Gini_us* 0.692 5 riziko 0.687 UMUTeam* (García-Díaz et al, 2022) 0.687 Tathagata Raha* (Raha et al, 2022) 0.687 LastResort* (Agrawal and Mamidi, 2022) 0.686 TeamOtter* (Maheshwari and Nangi, 2022) 0.679 ShailyDesai 0.677 JRLV* (Ravagli and Vaiani, 2022) 0.670 I2C* (Cordon et al, 2022) 0.665 qinian* (Gu et al, 2022a) 0…”

mentioning

confidence: 99%

See 1 more Smart Citation

SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification

Fersini¹,

Gasparini²,

Rizzi³

et al. 2022

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

View full text Add to dashboard Cite

The paper describes the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI),which explores the detection of misogynous memes on the web by taking advantage of available texts and images. The task has been organised in two related sub-tasks: the first one is focused on recognising whether a meme is misogynous or not (Sub-task A), while the second one is devoted to recognising types of misogyny (Sub-task B). MAMI has been one of the most popular tasks at SemEval-2022 with more than 400 participants, 65 teams involved in Sub-task A and 41 in Sub-task B from 13 countries. The MAMI challenge received 4214 submitted runs (of which 166 uploaded on the leader-board), denoting an enthusiastic participation for the proposed problem. The collection and annotation is described for the task dataset. The paper provides an overview of the systems proposed for the challenge, reports the results achieved in both sub-tasks and outlines a description of the main errors for a comprehension of the systems capabilities and for detailing future research perspectives.

show abstract

Section: C2 Leader-board Of Sub-task Bmentioning

confidence: 99%

mentioning

confidence: 99%

SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification

Fersini¹,

Gasparini²,

Rizzi³

et al. 2022

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

View full text Add to dashboard Cite

show abstract

“…To assess the effectiveness of the textbased approach, the authors conducted training on a range of language models, which included RNN-based models, RoBERTa, and Ernie-2.0. A multimodal BERT architecture that utilises images and text data by incorporating world knowledge from pre-trained CLIP and Urban dictionary is discussed by Maheshwari et al [36]. For classification tasks based on visual and textual features, simple machine learning baselines, namely SVM, Naive Bayes, and Logistic Regression models are being used.…”

Section: Related Workmentioning

confidence: 99%

DeVi Deep Learning Framework for Misogyny Identification in Multimodal Data

Singh,

Das,

Manderna

et al. 2023

Preprint

View full text Add to dashboard Cite

In recent times, there has been a notable upsurge in the frequency of memes across a wide range of social media platforms. Memes provide amusement to people with their humour, but unfortunately, some memes exploit this humour as a cover to spread misogynistic and hateful content targeting women on online platforms. Most of the previously proposed methods for detecting misogyny have primarily concentrated on either textual or visual content. However, there is a noticeable dearth of research on analysing multimodal data that combines both images and text. We propose a DeVi framework comprising DeBERTa and Vision Transformer with an attention-based late fusion strategy for automatic misogyny identification in memes. We evaluated the proposed framework on two different subtasks provided in SemEval-2022 task 5 on the MAMI dataset. Subtask A is a misogynous meme identification task, and subtask B is to identify the type of misogyny, which is a multilabel classification task. The proposed framework achieved an F1-score of 0.865 and 0.783 on subtask A and B, respectively. The experimental findings clearly illustrate that the DeVi framework we propose outperforms existing multimodal models in both subtasks, showcasing its superior performance. This highlights the effectiveness and adaptability of the DeVi framework.

show abstract