Papers by Hardik Chauhan
MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations (2020.coling-main)
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| Challenge: | Emotion and sentiment classification in dialogues has gained popularity in recent times . a number of datasets are imbalanced in representing different emotions and consist of an only single emotion. |
| Approach: | They propose to use a dataset to analyze emotions and sentiments in dialogues . they use text, audio and video to identify the correct emotions with the appropriate intensity and sentiment in an utterance of a dialogue . |
| Outcome: | The proposed datasets are balanced in representing different emotions and consist of only one emotion. |
Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System (P19-1)
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| Challenge: | Existing multimodal dialogue systems are based on unimodal sources, capturing information from text and image. |
| Approach: | They propose a position and attribute aware attention mechanism to learn enhanced image representation conditioned on the user utterance. |
| Outcome: | The proposed model outperforms the state-of-the-art models on text similarity metrics. |
Reinforced Multi-task Approach for Multi-hop Question Generation (2020.coling-main)
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| Challenge: | Empirical evaluation shows our model to outperform the single-hop question generation models on both automatic evaluation metrics such as BLEU, METEOR, and ROUGE and human evaluation metrics for quality and coverage of the generated questions. |
| Approach: | They propose a question-aware reward function to maximize the utilization of supporting facts in the context. |
| Outcome: | The proposed model outperforms single-hop neural question generation models on automatic evaluation metrics and human evaluation metrics for quality and coverage of the generated questions. |
DUBLIN: Visual Document Understanding By Language-Image Network (2023.emnlp-industry)
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Kriti Aggarwal, Aditi Khandelwal, Kumar Tanmay, Owais Khan Mohammed, Qiang Liu, Monojit Choudhury, Hardik Chauhan, Subhojit Som, Vishrav Chaudhary, Saurabh Tiwary
| Challenge: | DUBLIN is a pixel-based visual document understanding model that does not rely on OCR. |
| Approach: | They propose a pixel-based visual document understanding model that does not rely on OCR. |
| Outcome: | The proposed model performs on extractive tasks such as DocVQA, InfoVQA and AI2D, and strong performance on abstraction datasets such as VisualMRC and text captioning. |