Papers by Gaurav Pandey
Exemplar Encoder-Decoder for Neural Conversation Generation (P18-1)
Copied to clipboard
| Challenge: | Existing approaches to generate conversational systems suffer from lack of diversity in responses and generation of short, repetitive and uninteresting responses. |
| Approach: | They propose a novel conversation model that uses similar examples from training data to generate responses. |
| Outcome: | The proposed model outperforms state-of-the-art sequence to sequence learning on several evaluation metrics on two large data sets. |
Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG (2025.findings-naacl)
Copied to clipboard
Kushagra Bhushan, Yatin Nandwani, Dinesh Khandelwal, Sonam Gupta, Gaurav Pandey, Dinesh Raghu, Sachindra Joshi
| Challenge: | Retrieval-Augmented Generation (RAG) enhances response relevance by incorporating retrieved domain knowledge in the context, retrieval errors can still lead to hallucinations and incorrect answers. |
| Approach: | They propose a framework that augments the learning process by context augmentation and knowledge paraphrasing by incorporating retrieved domain knowledge into the context. |
| Outcome: | The proposed framework achieves 10% relative gain in token-level recall while preserving the LLM’s generalization capabilities. |
Agent Assist through Conversation Analysis (2020.emnlp-demos)
Copied to clipboard
Kshitij Fadnis, Nathaniel Mills, Jatin Ganhotra, Haggai Roitman, Gaurav Pandey, Doron Cohen, Yosi Mass, Shai Erera, Chulaka Gunasekara, Danish Contractor, Siva Patel, Q. Vera Liao, Sachindra Joshi, Luis Lastras, David Konopnicki
| Challenge: | Using conversational approach to information retrieval for agent assistance, customer support agents are a critical part of an organization's customer support team. |
| Approach: | They propose a conversational approach to information retrieval for agent assistance that monitors an evolving conversation and recommends both responses and URLs of documents. |
| Outcome: | The proposed system monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. |
Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems (2022.emnlp-industry)
Copied to clipboard
| Challenge: | Goal-oriented dialog systems fail to recognize the intent of natural language requests due to system errors, incomplete service coverage, or insufficient training. |
| Approach: | They propose an end-to-end pipeline for processing unrecognized user utterances, deployed in a commercial task-oriented dialog system, including a specifically-tailored clustering algorithm, a novel approach to cluster representative extraction, and cluster naming. |
| Outcome: | The proposed components show that they improve the performance of the proposed system in the analysis of unrecognized user requests. |
Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Popular dialog datasets such as MultiWOZ are created by providing crowd workers with instructions that describe the task to be accomplished. |
| Approach: | They propose a data creation strategy that uses a pre-trained language model to simulate the interaction between crowd workers by creating a user bot and an agent bot. |
| Outcome: | The proposed data creation strategy improves on two publicly available datasets using a pre-trained language model and a smaller percentage of actual crowd-generated conversations and their corresponding instructions. |
Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches for dialog response retrieval embed the context-response pairs as points in the embedding space. |
| Approach: | They propose a scalable model that can learn complex relationships between context-response pairs . they train the models by optimizing the Kullback-Leibler divergence between the distributions induced by context-responders in the training data. |
| Outcome: | The proposed model performs better than other embedding-based approaches on public conversation data. |
HealthAlignSumm : Utilizing Alignment for Multimodal Summarization of Code-Mixed Healthcare Dialogues (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Collaboration between doctors and AI scientists is leading to personalized models to stream-line healthcare tasks and improve productivity. |
| Approach: | They propose to use alignment techniques to combine a doctor-patient dialogue with a visual component of the BART model. |
| Outcome: | The proposed model in-tegrates visual components with the BART ar-chitecture. |