Papers by Mukund Sridhar
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering (2021.eacl-main)
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| Challenge: | Existing methods for Dialog State Tracking do not generalize well to new domains and unseen slots. |
| Approach: | They propose an ontology-free framework that queries for unseen constraints and slots in multi-domain task-oriented dialogs using a conditional language model pre-trained on substantive English sentences. |
| Outcome: | The proposed framework improves goal accuracy in zero-shot domain adaptation settings by up to 9% over the previous state-of-the-art on the MultiWOZ 2.1 dataset. |
Low-Resource Compositional Semantic Parsing with Concept Pretraining (2023.eacl-main)
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| Challenge: | Semantic parsing is a key role in voice assistants by mapping natural language to structured meaning representations. |
| Approach: | They propose an architecture to perform domain adaptation automatically with only a small amount of metadata about the new domain and without any new training data. |
| Outcome: | The proposed architecture outperforms existing models in low-resource settings. |
An Empirical Analysis of Leveraging Knowledge for Low-Resource Task-Oriented Semantic Parsing (2023.findings-acl)
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Mayank Kulkarni, Aoxiao Zhong, Nicolas Guenon des mesnards, Sahar Movaghati, Mukund Sridhar, He Xie, Jianhua Lu
| Challenge: | Task-oriented semantic parsing is a new approach to represent the meaning of user requests with arbitrarily nested semantics. |
| Approach: | They propose to use knowledge-enhanced encoders to parse user requests with arbitrarily nested semantics. |
| Outcome: | The proposed model improves performance in low-resource and low-compute settings. |
Towards Realistic Single-Task Continuous Learning Research for NER (2021.findings-emnlp)
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Justin Payan, Yuval Merhav, He Xie, Satyapriya Krishna, Anil Ramakrishna, Mukund Sridhar, Rahul Gupta
| Challenge: | Academic datasets are often static and contain data that is annotated all at once based on fixed annotation guidelines. |
| Approach: | They propose to build a single-task continuous learning dataset from an existing dataset and release it along with the code to the research community. |
| Outcome: | The proposed model is based on an existing dataset and released to the research community. |
Instilling Type Knowledge in Language Models via Multi-Task QA (2022.findings-naacl)
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| Challenge: | Current methods to learn entity types rely on coarse, noisy labels . current methods rely only on text-to-text pre-training on type-centric questions . |
| Approach: | They propose to instill fine-grained type knowledge in language models by pre-training on type-centric questions. |
| Outcome: | The proposed model achieves state-of-the-art in zero-shot dialog state tracking benchmarks and can accurately infer entity types in Wikipedia articles. |