Papers by Gautam Shroff
Program Synthesis for Complex QA on Charts via Probabilistic Grammar Based Filtered Iterative Back-Translation (2023.findings-eacl)
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Shabbirhussain Bhaisaheb, Shubham Paliwal, Rajaswa Patil, Manasi Patwardhan, Lovekesh Vig, Gautam Shroff
| Challenge: | Current chart-based Question Answering approaches address structural, visual or simple data retrieval-type questions with fixed-vocabulary answers. |
| Approach: | They employ a neural semantic parser to transform NL questions into SQL programs . they use a probabilistic context-free grammar to generate NL queries from a schema . |
| Outcome: | The proposed approach achieves State-of-the-Art (SOTA) results on reasoning-based queries. |
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. |