Papers by Mohammed Moshiul Hoque
A Multimodal Framework to Detect Target Aware Aggression in Memes (2024.eacl-long)
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| Challenge: | Recent research on memes’ detrimental facets is skewed towards high-resource languages, such as Bengali. |
| Approach: | They propose a dataset MIMOSA that annotates annotated memes across five aggression target categories in Bengali and propose 'Multimodal Attentive Fusion' to detect aggression targets. |
| Outcome: | The proposed method outperforms state-of-the-art methods in Bengali and in low-resource languages. |
MDC3: A Novel Multimodal Dataset for Commercial Content Classification in Bengali (2025.naacl-srw)
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| Challenge: | Identifying commercial posts in resource-constrained languages remains a challenge for automatic text classification tasks. |
| Approach: | They propose a dataset for Bengali social media posts classified as commercial and noncommercial . they include an annotation guideline to aid future dataset creation in resource-constrained languages . |
| Outcome: | The proposed dataset is based on an annotation guideline for future dataset creation in resource-constrained languages. |
BenNumEval: A Benchmark to Assess LLMs’ Numerical Reasoning Capabilities in Bengali (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) excel in general-purpose tasks but struggle with numerical reasoning, especially in low-resource languages like Bengali. |
| Approach: | They propose a benchmark to assess LLMs on numerical reasoning tasks in Bengali. |
| Outcome: | The proposed benchmark assesses LLMs on numerical reasoning tasks in Bengali. |
Deciphering Hate: Identifying Hateful Memes and Their Targets (2024.acl-long)
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| Challenge: | a growing body of research has focused on the negative aspects of memes in high-resource languages like Bengali . a new dataset for Bengali hateful memes is designed to detect their targeted entities . |
| Approach: | They propose a multimodal dataset that analyzes the modality of memes and compares them with other datasets. |
| Outcome: | The proposed dataset outperforms state-of-the-art datasets on Bengali hateful memes . the proposed dataset is generalizable on other low-resource hateful memes datasets compared with baselines based on the proposed model . |
MemoSen: A Multimodal Dataset for Sentiment Analysis of Memes (2022.lrec-1)
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| Challenge: | Recent studies on sentiment analysis of memes have focused on English, but there is a significant barrier to performing multimodal sentiment analysis research in resource-constrained languages like Bengali. |
| Approach: | They propose to use a Bengali dataset to perform multimodal sentiment analysis in low resource languages. |
| Outcome: | The proposed dataset for Bengali contains 4417 memes with three annotated labels positive, negative, and neutral. |
Emotion Classification in a Resource Constrained Language Using Transformer-based Approach (2021.naacl-srw)
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| Challenge: | Existing methods to classify Bengali text into six basic emotions are infancy for resource-constrained languages like English, Arabic, Chinese and French. |
| Approach: | They propose a transformer-based technique to classify Bengali text into one of the six basic emotions: anger, fear, disgust, sadness, joy, and surprise. |
| Outcome: | The proposed technique outperforms all other techniques by achieving highest weighted f_1-score on the test data. |
MUTE: A Multimodal Dataset for Detecting Hateful Memes (2022.aacl-srw)
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| Challenge: | social media has enabled information propagation at unprecedented rate, but also generated malign content, such as hateful memes . a multimodal hate speech dataset is used to study the impact of hateful content on society . current studies focus on monolingual memes, but existing models cannot provide accurate inferences based on code-mixed captions a study on Bengali memes shows that joint evaluation of visual and textual features significantly improves the hateful data classification . |
| Approach: | They propose to use a multimodal hate speech dataset to detect hateful memes . they use monolingual captions in English and Bengali to analyze the content . |
| Outcome: | The proposed dataset shows that evaluation of visual and textual features significantly improves the hateful memes classification compared to unimodal evaluation. |
Align before Attend: Aligning Visual and Textual Features for Multimodal Hateful Content Detection (2024.eacl-srw)
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| Challenge: | Existing approaches to multimodal hateful content detection focus on detecting hate speech from text-based content, but they fail to address modality-specific features. |
| Approach: | They propose a context-aware attention framework for multimodal hateful content detection that integrates an attention layer to meaningfully align the visual and textual features. |
| Outcome: | The proposed framework achieves F1-scores of 69.7% and 70.3% on two hateful meme datasets and shows 2.5% and 3.2% performance improvement over the state-of-the-art systems. |