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Register a new userFindings of the WOAH 5 Shared Task on Fine Grained Hateful Memes Detection
النتائج التي توصل إليها WOAH 5 المهمة المشتركة على الكشف عن الميمات البغيضة
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نقدم النتائج والنتائج الرئيسية للمهمة المشتركة في WOAH 5 على الكشف عن الميمات البغيضة.تتضمن المهمة ملاحقتين فرعيين يتعلق بالتحديات المتميزة في الكشف الدقيق للميمات البغيضة: (1) الفئة المحمية تعرضت لها MEME و (2) نوع الهجوم.3 فرق قدم وصف نظام وصف النظام.تعتمد هذه المهمة المشتركة على مهمة الكشف عن الأيمن التي تم إنشاؤها بواسطة بحث Facebook AI في عام 2020.
We present the results and main findings of the shared task at WOAH 5 on hateful memes detection. The task include two subtasks relating to distinct challenges in the fine-grained detection of hateful memes: (1) the protected category attacked by the meme and (2) the attack type. 3 teams submitted system description papers. This shared task builds on the hateful memes detection task created by Facebook AI Research in 2020.
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We present the results and the main findings of the NLP4IF-2021 shared tasks. Task 1 focused on fighting the COVID-19 infodemic in social media, and it was offered in Arabic, Bulgarian, and English. Given a tweet, it asked to predict whether that twe
et contains a verifiable claim, and if so, whether it is likely to be false, is of general interest, is likely to be harmful, and is worthy of manual fact-checking; also, whether it is harmful to society, and whether it requires the attention of policy makers. Task 2 focused on censorship detection, and was offered in Chinese. A total of ten teams submitted systems for task 1, and one team participated in task 2; nine teams also submitted a system description paper. Here, we present the tasks, analyze the results, and discuss the system submissions and the methods they used. Most submissions achieved sizable improvements over several baselines, and the best systems used pre-trained Transformers and ensembles. The data, the scorers and the leaderboards for the tasks are available at http://gitlab.com/NLP4IF/nlp4if-2021.
The Shared Task on Hateful Memes is a challenge that aims at the detection of hateful content in memes by inviting the implementation of systems that understand memes, potentially by combining image and textual information. The challenge consists of
three detection tasks: hate, protected category and attack type. The first is a binary classification task, while the other two are multi-label classification tasks. Our participation included a text-based BERT baseline (TxtBERT), the same but adding information from the image (ImgBERT), and neural retrieval approaches. We also experimented with retrieval augmented classification models. We found that an ensemble of TxtBERT and ImgBERT achieves the best performance in terms of ROC AUC score in two out of the three tasks on our development set.
Hope is considered significant for the well-being, recuperation and restoration of human life by health professionals. Hope speech reflects the belief that one can discover pathways to their desired objectives and become roused to utilise those pathw
ays. To encourage research in natural language processing towards positive reinforcement approach, we created a hope speech detection dataset. This paper reports on the shared task of hope speech detection for Tamil, English, and Malayalam languages. The shared task was conducted as a part of the EACL 2021 workshop on Language Technology for Equality, Diversity, and Inclusion (LT-EDI-2021). We summarize here the datasets for this challenge which are openly available at https://competitions.codalab.org/competitions/27653, and present an overview of the methods and the results of the competing systems. To the best of our knowledge, this is the first shared task to conduct hope speech detection.
Hateful memes pose a unique challenge for current machine learning systems because their message is derived from both text- and visual-modalities. To this effect, Facebook released the Hateful Memes Challenge, a dataset of memes with pre-extracted te
xt captions, but it is unclear whether these synthetic examples generalize to memes in the wild'. In this paper, we collect hateful and non-hateful memes from Pinterest to evaluate out-of-sample performance on models pre-trained on the Facebook dataset. We find that memes in the wild' differ in two key aspects: 1) Captions must be extracted via OCR, injecting noise and diminishing performance of multimodal models, and 2) Memes are more diverse than traditional memes', including screenshots of conversations or text on a plain background. This paper thus serves as a reality-check for the current benchmark of hateful meme detection and its applicability for detecting real world hate.
This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021. We build our system on top of a state-of-the-art system for binary hateful meme classification that already uses image tags s
uch as race, gender, and web entities. We add further metadata such as emotions and experiment with data augmentation techniques, as hateful instances are underrepresented in the data set.
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