Papers by Gopendra Vikram Singh
A Sentiment and Emotion Aware Multimodal Multiparty Humor Recognition in Multilingual Conversational Setting (2022.coling-1)
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| Challenge: | Humor is an essential aspect of daily conversation, and people try to provoke humor in their talks. |
| Approach: | They propose a multitask framework that annotates Hindi utterances with sentiment and emotion classes. |
| Outcome: | The proposed framework improves on the recently released Hindi Humor dataset . it takes sentiment and emotion into account to understand humor . |
Just a Scratch: Enhancing LLM Capabilities for Self-harm Detection through Intent Differentiation and Emoji Interpretation (2025.acl-long)
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| Challenge: | Self-harm detection on social media is critical for early intervention and mental health support, yet remains challenging due to the subtle, context-dependent nature of such expressions. |
| Approach: | They propose a framework to distinguish intent through nuanced language–emoji interplay. |
| Outcome: | The proposed framework improves self-harm detection and explanation tasks on three state-of-the-art LLMs. |
EmoInHindi: A Multi-label Emotion and Intensity Annotated Dataset in Hindi for Emotion Recognition in Dialogues (2022.lrec-1)
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| Challenge: | Existing datasets for emotion recognition in dialogues are in English . existing datasets are limited to a few languages like Hindi . |
| Approach: | They propose a large conversational dataset in Hindi for multi-label emotion and intensity recognition in conversations . they use a Wizard-of-Oz manner to annotate dialogues with 16 emotion labels . |
| Outcome: | The proposed dataset contains 1,814 dialogues with 44,247 utterances in Hindi . it is based on a Wizard-of-Oz manner and can detect emotions in conversation . |
COMMA-DEER: COmmon-sense Aware Multimodal Multitask Approach for Detection of Emotion and Emotional Reasoning in Conversations (2022.coling-1)
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| Challenge: | Mental health is a critical component of the United Nations’ Sustainable Development Goals (SDGs), particularly Goal 3 which aims to provide “good health and well-being”. |
| Approach: | They propose a task of detecting emotional reasoning and accompanying emotions in conversations that is manually annotated at the utterance level. |
| Outcome: | The proposed model achieves 6% accuracy and 4.62% accuracy on the emotion detection task and 3.56% accuracy, and 3.31% F1 on the ER detection task, compared to the existing state-of-the-art model. |