UniBO at SemEval-2022 Task 5: A Multimodal bi-Transformer Approach to the Binary and Fine-grained Identification of Misogyny in Memes

Muti, Arianna; Korre, Katerina; Barrón-Cedeño, Alberto

doi:10.18653/v1/2022.semeval-1.91

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…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…”

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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)

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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.

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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

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MIMIC: Misogyny Identification in Multimodal Internet Content in Hindi-English Code-Mixed Language

Singh,

Sharma,

Singh

2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Over the years, social media has emerged as one of the most popular platforms where people express their views and share thoughts about various aspects. The social media content now includes a variety of components such as text, images, videos etc. One type of interest is memes, which often combine text and images. It is relevant to mention here that, social media being an unregulated platform, sometimes also has instances of discriminatory, offensive and hateful content being posted. Such content adversely affects the online well-being of the users. Therefore, it is very important to develop computational models to automatically detect such content so that appropriate corrective action can be taken. Accordingly, there have been research efforts on automatic detection of such content focused mainly on the texts. However, the fusion of multimodal data (as in memes) creates various challenges in developing computational models that can handle such data, more so in the case of low-resource languages. Among such challenges, the lack of suitable datasets for developing computational models for handling memes in low-resource languages is a major problem. This work attempts to bridge the research gap by providing a large-sized curated dataset comprising 5,054 memes in Hindi-English code-mixed language, which are manually annotated by three independent annotators. It comprises two subtasks: (i) Subtask-1 (Binary classification involving tagging a meme as misogynous or non-misogynous), and (ii) Subtask-2 (multi-label classification of memes into different categories). The data quality is evaluated by computing Krippendorff's alpha. Different computational models are then applied on the data in three settings: text-only, image-only, and multimodal models using fusion techniques. The results show that the proposed multimodal method using the fusion technique may be the preferred choice for the identification of misogyny in multimodal Internet content and that the dataset is suitable for advancing research and development in the area.

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UniBO at SemEval-2022 Task 5: A Multimodal bi-Transformer Approach to the Binary and Fine-grained Identification of Misogyny in Memes

Cited by 2 publications

References 18 publications

SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification

SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification

MIMIC: Misogyny Identification in Multimodal Internet Content in Hindi-English Code-Mixed Language

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