Papers by Tulika Saha
Action and Reaction Go Hand in Hand! a Multi-modal Dialogue Act Aided Sarcasm Identification (2024.lrec-main)
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| Challenge: | Existing studies have shown that sarcasm is reflected by the intended meaning of the speaker's utterance. |
| Approach: | They propose to extend the MUStARD dataset to enclose dialogue acts for each dialogue . they propose a dialogue act-aided multi-modal transformer network for sarcasm identification model . |
| Outcome: | The proposed model improves performance in dialogue act-aided sarcasm identification compared to sardasmatic identification alone. |
Towards Sentiment and Emotion aided Multi-modal Speech Act Classification in Twitter (2021.naacl-main)
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| Challenge: | Speech Act Classification determining the communicative intent of an utterance has been investigated widely over the years as a standalone task. |
| Approach: | They propose a multi-modal, emotion-TA dataset called EmoTA from open-source Twitter dataset and a Dyadic Attention Mechanism framework that integrates intra-modal and inter-modal attention to fuse multiple modalities. |
| Outcome: | The proposed framework boosts the performance of the primary task, i.e., TA classification (TAC), by benefitting from the two secondary tasks, namely, Sentiment and Emotion Analysis compared to its uni-modal and single task TAC variants. |
Can you Summarize my learnings? Towards Perspective-based Educational Dialogue Summarization (2023.findings-emnlp)
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| Challenge: | Increasing use of virtual tutors has allowed for more efficient, personalized, and interactive AI-based learning experiences. |
| Approach: | They propose a task of Multi-modal Perspective based Dialogue Summarization (MM-PerSumm) that summarizes educational dialogues from three unique perspectives: the Student, the Tutor, and a Generic viewpoint. |
| Outcome: | The proposed model can summarize educational dialogues from three perspectives, while student-oriented summaries should distill learning points, track progress, and suggest scope for improvement. |
Towards Emotion-aided Multi-modal Dialogue Act Classification (2020.acl-main)
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| Challenge: | Considerable work on Dialogue Act Classification (DAC) has been done on textual inputs. |
| Approach: | They propose to use a multimodal Emotion aware Dialogue Act dataset to explore the role of multi-modality and emotion recognition in DAC. |
| Outcome: | The proposed dataset shows that multi-modality and emotion recognition improves DAC performance compared to uni-modal and single task DAC variants. |
GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization (2022.coling-1)
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| Challenge: | Existing approaches to capture and integrate global semantic information are limited due to their limited ability to capture long-range dependencies. |
| Approach: | They propose a graph contrastive topic enhanced language model that integrates a neural topic model with a pre-trained language model to capture global contextual semantics. |
| Outcome: | The proposed model outperforms existing methods on general domain and biomedical datasets. |
A Shoulder to Cry on: Towards A Motivational Virtual Assistant for Assuaging Mental Agony (2022.naacl-main)
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| Challenge: | Mental health disorders are one of the primary causes of disability worldwide . lack of qualified and competent mental health professionals is a major problem . we propose a virtual assistant that can act as the first point of contact and comfort for mental health patients. |
| Approach: | They propose a virtual assistant that can act as the first point of contact and comfort for mental health patients. |
| Outcome: | The proposed system outperforms baselines in the evaluation of 7k dyadic conversations from a peer-to-peer support platform. |
Meta-Learning based Deferred Optimisation for Sentiment and Emotion aware Multi-modal Dialogue Act Classification (2022.aacl-main)
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| Challenge: | Empirically, we show that the optimisation of multi-modal DAC, SA and ER tasks produces better results compared to its different counterparts. |
| Approach: | They propose a dual attention mechanism that integrates sentiment tags into a multi-modal conversational framework that integrate modal attentions and multiple loss optimization. |
| Outcome: | The proposed framework integrates sentiment tags for each utterance and learns generalized features across multiple tasks. |