UVCE-IIITT@DravidianLangTech-EACL2021: Tamil Troll Meme Classification: You need to Pay more Attention

• URL: http://arxiv.org/abs/2104.09081v1
• Date: Mon, 19 Apr 2021 06:57:43 GMT
• Title: UVCE-IIITT@DravidianLangTech-EACL2021: Tamil Troll Meme Classification: You need to Pay more Attention
• Authors: Siddhanth U Hegde, Adeep Hande, Ruba Priyadharshini, Sajeetha Thavareesan, Bharathi Raja Chakravarthi
• Abstract summary: We try to analyze the true meaning of Tamil memes by categorizing them as troll and non-troll. The dataset consists of troll and non-troll images with their captions as text. The objective of the model is to pay more attention to the extracted features and to ignore the noise in both images and text.
• Score: 0.19573380763700712
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tamil is a Dravidian language that is commonly used and spoken in the southern part of Asia. In the era of social media, memes have been a fun moment in the day-to-day life of people. Here, we try to analyze the true meaning of Tamil memes by categorizing them as troll and non-troll. We propose an ingenious model comprising of a transformer-transformer architecture that tries to attain state-of-the-art by using attention as its main component. The dataset consists of troll and non-troll images with their captions as text. The task is a binary classification task. The objective of the model is to pay more attention to the extracted features and to ignore the noise in both images and text.

Related papers

• MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models [50.2355423914562]
  We introduce MemeReaCon, a novel benchmark designed to evaluate how Large Vision Language Models (LVLMs) understand memes in their original context.<n>We collected memes from five different Reddit communities, keeping each meme's image, the post text, and user comments together.<n>Our tests with leading LVLMs show a clear weakness: models either fail to interpret critical information in the contexts, or overly focus on visual details while overlooking communicative purpose.
  arXiv  Detail & Related papers  (2025-05-23T03:27:23Z)
• XMeCap: Meme Caption Generation with Sub-Image Adaptability [53.2509590113364]
  Humor, deeply rooted in societal meanings and cultural details, poses a unique challenge for machines. We introduce the textscXMeCap framework, which adopts supervised fine-tuning and reinforcement learning. textscXMeCap achieves an average evaluation score of 75.85 for single-image memes and 66.32 for multi-image memes, outperforming the best baseline by 3.71% and 4.82%, respectively.
  arXiv  Detail & Related papers  (2024-07-24T10:51:46Z)
• See It from My Perspective: Diagnosing the Western Cultural Bias of Large Vision-Language Models in Image Understanding [78.88461026069862]
  Vision-language models (VLMs) can respond to queries about images in many languages. We present a novel investigation that demonstrates and localizes Western bias in image understanding.
  arXiv  Detail & Related papers  (2024-06-17T15:49:51Z)
• An image speaks a thousand words, but can everyone listen? On image transcreation for cultural relevance [53.974497865647336]
  We take a first step towards translating images to make them culturally relevant. We build three pipelines comprising state-of-the-art generative models to do the task. We conduct a human evaluation of translated images to assess for cultural relevance and meaning preservation.
  arXiv  Detail & Related papers  (2024-04-01T17:08:50Z)
• Deciphering Hate: Identifying Hateful Memes and Their Targets [4.574830585715128]
  We introduce a novel dataset for detecting hateful memes in Bengali, BHM (Bengali Hateful Memes) The dataset consists of 7,148 memes with Bengali as well as code-mixed captions, tailored for two tasks: (i) detecting hateful memes, and (ii) detecting the social entities they target. To solve these tasks, we propose DORA, a multimodal deep neural network that systematically extracts the significant modality features from the memes.
  arXiv  Detail & Related papers  (2024-03-16T06:39:41Z)
• Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest [70.40189243067857]
  Large neural networks can now generate jokes, but do they really "understand" humor? We challenge AI models with three tasks derived from the New Yorker Cartoon Caption Contest. We find that both types of models struggle at all three tasks.
  arXiv  Detail & Related papers  (2022-09-13T20:54:00Z)
• Overview of Abusive and Threatening Language Detection in Urdu at FIRE 2021 [50.591267188664666]
  We present two shared tasks of abusive and threatening language detection for the Urdu language. We present two manually annotated datasets containing tweets labelled as (i) Abusive and Non-Abusive, and (ii) Threatening and Non-Threatening. For both subtasks, m-Bert based transformer model showed the best performance.
  arXiv  Detail & Related papers  (2022-07-14T07:38:13Z)
• Hate Me Not: Detecting Hate Inducing Memes in Code Switched Languages [1.376408511310322]
  In countries like India, where multiple languages are spoken, these abhorrent posts are from an unusual blend of code-switched languages. This hate speech is depicted with the help of images to form "Memes" which create a long-lasting impact on the human mind. We take up the task of hate and offense detection from multimodal data, i.e. images (Memes) that contain text in code-switched languages.
  arXiv  Detail & Related papers  (2022-04-24T21:03:57Z)
• hate-alert@DravidianLangTech-ACL2022: Ensembling Multi-Modalities for Tamil TrollMeme Classification [5.51252705016179]
  We explore several models to detect Troll memes in Tamil based on the shared task, "Troll Meme Classification in DravidianLangTech2022" at ACL-2022. We observe while the text-based model MURIL performs better for Non-troll meme classification, the image-based model VGG16 performs better for Troll-meme classification. Our fusion model achieved a 0.561 weighted average F1 score and ranked second in this task.
  arXiv  Detail & Related papers  (2022-03-25T17:53:39Z)
• Do Images really do the Talking? Analysing the significance of Images in Tamil Troll meme classification [0.16863755729554888]
  We try to explore the significance of visual features of images in classifying memes. We try to incorporate the memes as troll and non-trolling memes based on the images and the text on them.
  arXiv  Detail & Related papers  (2021-08-09T09:04:42Z)
• Exploiting BERT For Multimodal Target SentimentClassification Through Input Space Translation [75.82110684355979]
  We introduce a two-stream model that translates images in input space using an object-aware transformer. We then leverage the translation to construct an auxiliary sentence that provides multimodal information to a language model. We achieve state-of-the-art performance on two multimodal Twitter datasets.
  arXiv  Detail & Related papers  (2021-08-03T18:02:38Z)
• Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset [47.65948529524281]
  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, and 2) Memes are more diverse than traditional memes', including screenshots of conversations or text on a plain background.
  arXiv  Detail & Related papers  (2021-07-09T09:04:05Z)
• NLP-CUET@DravidianLangTech-EACL2021: Investigating Visual and Textual Features to Identify Trolls from Multimodal Social Media Memes [0.0]
  A shared task is organized to develop models that can identify trolls from multimodal social media memes. This work presents a computational model that we have developed as part of our participation in the task. We investigated the visual and textual features using CNN, VGG16, Inception, Multilingual-BERT, XLM-Roberta, XLNet models.
  arXiv  Detail & Related papers  (2021-02-28T11:36:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.