Papers by Mayur Patidar
Do I have the Knowledge to Answer? Investigating Answerability of Knowledge Base Questions (2023.acl-long)
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| Challenge: | missing facts, incomplete schema and limited scope lead to many questions being unanswerable. |
| Approach: | They propose to adapt a KBQA dataset with unanswerable questions to detect missing facts and incomplete schema. |
| Outcome: | The proposed model performs poorly even after adaptation for unanswerable questions. |
Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning (2024.acl-long)
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| Challenge: | Existing Knowledge Base Question Answering (KBQA) architectures are expensive and time-consuming to deploy. |
| Approach: | They propose a KBQA architecture that performs KB-retrieval using multiple source-trained retrievers and re-ranks using an LLM. |
| Outcome: | The proposed architecture outperforms adaptations of SoTA KBQA models when training data is limited. |
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering (2022.naacl-main)
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| Challenge: | Existing approaches to intent detection rely on epoch wise clustering and classification based on labeled and unlabeled data. |
| Approach: | They propose an end-to-end deep contrastive clustering algorithm that jointly updates model parameters and cluster centers via supervised and self-supervised learning. |
| Outcome: | The proposed approach outperforms baselines on five public datasets and human-in-the-loop variant for practical deployment. |
Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection (2022.findings-naacl)
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| Challenge: | Existing methods to identify all possible user intents at design time are expensive and require storage of past data. |
| Approach: | They propose to continually train an intent detector on new intents while maintaining performance on prior intents. |
| Outcome: | The proposed method outperforms exemplar replay-based approaches on lifelong intent detection tasks and achieves state-of-the-art on four public datasets. |
From Monolingual to Multilingual FAQ Assistant using Multilingual Co-training (D19-61)
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Mayur Patidar, Surabhi Kumari, Manasi Patwardhan, Shirish Karande, Puneet Agarwal, Lovekesh Vig, Gautam Shroff
| Challenge: | Recent research on cross-lingual transfer shows state-of-the-art results on benchmark datasets using pre-trained language representation models like BERT. |
| Approach: | They propose a method to augment an annotated dataset with machine translations in target languages and fine-tune the PLRM jointly. |
| Outcome: | The proposed approach provides consistent gains on multiple benchmark datasets while requiring a single model for multiple languages. |
Complex Question Answering on knowledge graphs using machine translation and multi-task learning (2021.eacl-main)
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Saurabh Srivastava, Mayur Patidar, Sudip Chowdhury, Puneet Agarwal, Indrajit Bhattacharya, Gautam Shroff
| Challenge: | Existing approaches to question answering on knowledge graphs are based on a modularized sequential approach where errors in one module lead to the accumulation of errors in downstream modules. |
| Approach: | They propose a multi-task BERT based Neural Machine Translation model to address these challenges. |
| Outcome: | The proposed model can answer questions over a knowledge graph on one publicly available and one proprietary dataset. |