Papers by Shubhashis Sengupta
Persona or Context? Towards Building Context adaptive Personalized Persuasive Virtual Sales Assistant (2022.aacl-main)
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Abhisek Tiwari, Sriparna Saha, Shubhashis Sengupta, Anutosh Maitra, Roshni Ramnani, Pushpak Bhattacharyya
| Challenge: | Existing task-oriented conversational agents assume that end-users will always have a pre-determined and servable task goal, which results in dialogue failure in hostile scenarios, such as goal unavailability. |
| Approach: | They propose to build an end-to-end multi-modal persuasive dialogue system incorporating a personalized persuasive module aided goal controller and goal persuader. |
| Outcome: | The proposed system achieves user tasks even in goal unavailability scenarios by persuading them towards a similar and servable goal. |
COFAR: Commonsense and Factual Reasoning in Image Search (2022.aacl-main)
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Prajwal Gatti, Abhirama Subramanyam Penamakuri, Revant Teotia, Anand Mishra, Shubhashis Sengupta, Roshni Ramnani
| Challenge: | Existing approaches to retrieve relevant images for natural language searches are limited by visual recognition and lack of commonsense reasoning. |
| Approach: | They propose a framework that leverages visual content and natural language queries to enable commonsense reasoning and factual reasoning in the image search. |
| Outcome: | The proposed framework enables commonsense and factual reasoning in image search on a COFAR dataset. |
Intent Mining from past conversations for Conversational Agent (2020.coling-main)
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| Challenge: | Conversational systems are of primary interest in the AI community . many commercial bot building frameworks require a collection of user utterances and corresponding intent to train an intent model. |
| Approach: | They propose an intent discovery framework that can mine a vast amount of conversational logs and generate labeled data sets for training intent models. |
| Outcome: | The proposed framework can mine conversational logs and generate labeled data sets for training intent models. |
Hollywood Identity Bias Dataset: A Context Oriented Bias Analysis of Movie Dialogues (2022.lrec-1)
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Sandhya Singh, Prapti Roy, Nihar Sahoo, Niteesh Mallela, Himanshu Gupta, Pushpak Bhattacharyya, Milind Savagaonkar, Nidhi Sultan, Roshni Ramnani, Anutosh Maitra, Shubhashis Sengupta
| Challenge: | Movies reflect society and also hold power to transform opinions. |
| Approach: | They propose to annotate movie scripts for identity bias using a dataset that is annotated for gender, race/ethnicity, religion, age, occupation, LGBTQ, and other . |
| Outcome: | The proposed dataset contains dialogue turns annotated for gender, race/ethnicity, religion, age, occupation, LGBTQ, and other, which contains biases like body shaming, personality bias, etc. |
Constraint-based Multi-hop Question Answering with Knowledge Graph (2022.naacl-industry)
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| Challenge: | Recent work addresses multi-hop KGQA, which requires reasoning across numerous edges of the KG. |
| Approach: | They propose to use KG embeddings to reduce KG sparsity by performing missing link prediction. |
| Outcome: | Empirical results show that the proposed method produces state-of-the-art results on three KGQA datasets. |
TRIP NEGOTIATOR: A Travel Persona-aware Reinforced Dialogue Generation Model for Personalized Integrative Negotiation in Tourism (2024.findings-emnlp)
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Priyanshu Priya, Desai Yasheshbhai, Ratnesh Joshi, Roshni Ramnani, Anutosh Maitra, Shubhashis Sengupta, Asif Ekbal
| Challenge: | Incorporating traveler preferences, constraints, and expectations allows for customizing negotiation strategies, resulting in a more personalized and integrative experience. |
| Approach: | They propose a novel travel persona-aware Reinforced dIalogue generation model for personalized integrative negotiation in the tourism domain. |
| Outcome: | The proposed system generates coherent and diverse responses consistent with the traveler's personality. |
Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy (C18-1)
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Deepak Gupta, Rajkumar Pujari, Asif Ekbal, Pushpak Bhattacharyya, Anutosh Maitra, Tom Jain, Shubhashis Sengupta
| Challenge: | Existing QA systems that answer factual questions with short answers are rare in practice. |
| Approach: | They propose a proposed two-layered taxonomy technique for semantic question matching . they augment state-of-the-art deep learning models with question classes from a deep learning based question classifier . |
| Outcome: | The proposed technique achieves state-of-the-art on an open-domain dataset. |