Papers by Anjishnu Kumar
Learning to Retrieve Engaging Follow-Up Queries (2023.findings-eacl)
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Christopher Richardson, Sudipta Kar, Anjishnu Kumar, Anand Ramachandran, Zeynab Raeesy, Omar Khan, Abhinav Sethy
| Challenge: | Open domain conversational agents can answer a wide range of targeted queries, but knowledge exploration is a lengthy task. |
| Approach: | They propose a retrieval based system for predicting the next questions that the user might have . they train ranking models on a dataset called the Follow-up Query Bank . |
| Outcome: | The proposed system can proactively assist users in knowledge exploration leading to a more engaging dialog. |
Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning (D19-55)
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| Challenge: | Question Paraphrase Retrieval (QPR) systems can be used to answer rare and noisy reformulations of common questions by mapping them to a set of canonical forms. |
| Approach: | They propose a Question Paraphrase Retrieval (QPR) system that retrieves equivalent questions that result in the same answer as the original question. |
| Outcome: | The proposed system outperforms the standard loss function in NIR with noisy labels on two QPR datasets. |
Learning When Not to Answer: a Ternary Reward Structure for Reinforcement Learning Based Question Answering (N19-2)
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| Challenge: | Existing methods for question answering over knowledge graphs use reinforcement learning to reason over a knowledge graph. |
| Approach: | They propose a new performance metric for question-answering agents that extends the binary reward structure to a ternary reward structure which rewards an agent for not answering a question rather than giving an incorrect answer. |
| Outcome: | The proposed method significantly improves the precision of answered questions while only not answering a limited number of correctly answered questions. |
Efficient Large-Scale Neural Domain Classification with Personalized Attention (P18-1)
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| Challenge: | Using a scalable neural model, we show that personalization improves domain classification accuracy in a setting with thousands of overlapping domains. |
| Approach: | They propose a scalable neural model architecture with a shared encoder that incorporates personalization information and domain-specific classifiers that solves the problem efficiently. |
| Outcome: | The proposed architecture achieves two orders of magnitude faster than full model retraining. |