Papers by Md Shad Akhtar
Target-Augmented Shared Fusion-based Multimodal Sarcasm Explanation Generation (2025.findings-naacl)
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| Challenge: | Existing systems that generate sarcastic explanations neglect the significance of the intended irony in generating them. |
| Approach: | They propose a multimodal sarcasm explanation that leverages the inter-modality relationships between an image and its caption to learn the intended irony for explanations. |
| Outcome: | The proposed model outperforms existing models on the sarcastic post-processing task and shows that it is more accurate than existing models. |
Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech (2024.findings-acl)
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| Challenge: | Existing language models to generate implicit hate explanations are lacking in many fields. |
| Approach: | They propose to use language models to generate explicit hate posts to make it clear . they find that simpler models incorporating external toxicity signals outperform KG-infused models . |
| Outcome: | The proposed setup produces more precise explanations than zero-shot GPT-3.5, highlighting the intricate nature of the task. |
Knowledge Planning in Large Language Models for Domain-Aligned Counseling Summarization (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) exhibit remarkable capabilities in various generative tasks, but their adaptation to domain-specific intricacies remains challenging. |
| Approach: | They propose to use a planning engine to orchestrate structuring knowledge alignment to achieve high-order planning by encapsulating domain knowledge and leveraging sheaf convolution learning to enhance its understanding of the dialogue’s structural nuances. |
| Outcome: | The proposed framework improves on existing LLMs and shows that it can generate better summaries with better quality and better execution. |
Probing Critical Learning Dynamics of PLMs for Hate Speech Detection (2024.findings-eacl)
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| Challenge: | Existing studies on pretrained language models (PLMs) for hate speech detection have not investigated how their performance is affected by pretraining and finetuning. |
| Approach: | They propose to compare pretrained language models, evaluate their seed robustness, finetuning settings, and the impact of pretraining data collection time. |
| Outcome: | The proposed models show that they are more robust than other models and that they have a better chance of performing better than domain-specific models. |
DISARM: Detecting the Victims Targeted by Harmful Memes (2022.findings-naacl)
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| Challenge: | DISARM is a framework that uses named-entity recognition and person identification to detect all entities a meme is referring to and then incorporates a novel contextualized deep neural network to classify whether the meme intends to harm these entities. |
| Approach: | They propose a framework that uses named-entity recognition and person identification to detect all entities a meme is referring to and incorporates a novel contextualized deep neural network to classify whether the meme intends to harm them. |
| Outcome: | The proposed framework outperforms 10 unimodal and multimodal systems and reduces error rate of harmful target identification by 9 % absolute over baseline systems. |
Contextual Inter-modal Attention for Multi-modal Sentiment Analysis (D18-1)
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Deepanway Ghosal, Md Shad Akhtar, Dushyant Chauhan, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya
| Challenge: | Existing methods for multi-modal sentiment analysis are limited due to the use of text, visual and acoustic inputs. |
| Approach: | They propose a recurrent neural network based multi-modal attention framework that leverages contextual information for utterance-level sentiment prediction. |
| Outcome: | The proposed framework performs better on two multi-modal sentiment analysis benchmark datasets with accuracies of 82.31% and 79.80% for the MOSI and MOSEI datasets. |
When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues (2022.acl-long)
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| Challenge: | Indirect speech achieves a constellation of discourse goals in human communication, but it is challenging for AI agents to comprehend such idiosyncrasies. |
| Approach: | They propose a task to generate natural language explanations of satirical conversations using a multimodal and code-mixed dataset to capture multimodality. |
| Outcome: | The proposed task generates natural language explanations of satirical conversations in a multimodal and code-mixed setting and surpasses baselines on almost all metrics. |
Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities (2025.findings-emnlp)
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| Challenge: | Empirical results across four notable language models demonstrate significant improvements in attribute elicitation and user engagement. |
| Approach: | They propose a framework that identifies and prompts users to enrich their posts by eliciting missing support attributes. |
| Outcome: | The proposed framework improves engagement and elicits missing information from posts. |
No perspective, no perception!! Perspective-aware Healthcare Answer Summarization (2024.findings-acl)
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| Challenge: | Healthcare Community Question Answering forums are prone to off-topic discussions and diverse answers can be challenging for readers to sift through. |
| Approach: | They propose a task of perspective-specific answer summarization to identify different perspectives within healthcare-related responses and frame a perspective-driven abstractive summary covering all responses. |
| Outcome: | The proposed model outperforms existing models against five baselines and shows that it is more accurate than existing models. |
Intent-conditioned and Non-toxic Counterspeech Generation using Multi-Task Instruction Tuning with RLAIF (2024.naacl-long)
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| Challenge: | Existing systems that target hate speech with intent-conditioned counterspeech generate better results with longer contexts. |
| Approach: | They propose a framework that enables counterspeech generation by modeling the pragmatic implications underlying social biases in hateful statements. |
| Outcome: | The proposed framework outperforms existing benchmarks in intent-conditioned counterspeech generation. |
Measuring What Matters!! Assessing Therapeutic Principles in Mental-Health Conversation (2026.acl-long)
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| Challenge: | Recent systems exhibit conversational competence but lack structured mechanisms to evaluate adherence to core therapeutic principles. |
| Approach: | They propose a framework to evaluate therapist-like responses for clinically grounded appropriateness and effectiveness using an ordinal scale. |
| Outcome: | The proposed framework achieves an F-1 score of 63.34 versus the baseline Qwen3 score of 38.56 . |
Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality (N18-1)
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| Challenge: | Efficient word representations play an important role in solving various problems related to NLP, data mining, text mining etc. |
| Approach: | They propose to leverage bilingual word embeddings learned through a parallel corpus to minimize the effect of data sparsity. |
| Outcome: | The proposed model is tested against state-of-the-art methods in two experimental setups. |
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis (D19-1)
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| Challenge: | Multi-modal analysis is a field emerging in the fields of natural language processing, computer vision and speech processing . multimodal analysis uses a variety of information from multiple sources to build efficient systems . acoustic and visual information can provide better information for classification decisions . |
| Approach: | They propose a recurrent neural network based approach for multi-modal sentiment and emotion analysis . they employ a context-aware attention module to exploit the correspondence among neighboring utterances . |
| Outcome: | The proposed model learns inter-modal interaction among participating modalities through auto-encoder mechanism . it is compared with existing state-of-the-art models on five standard multi-modal affect analysis datasets . |
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis (N19-1)
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Md Shad Akhtar, Dushyant Chauhan, Deepanway Ghosal, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya
| Challenge: | Existing frameworks for sentiment and emotion analysis are not efficient for inter-task learning. |
| Approach: | They propose a multi-task learning framework that performs sentiment and emotion analysis together. |
| Outcome: | The proposed framework improves on a CMU-MOSEI dataset for sentiment and emotion analysis. |