Papers by Srinivas Bangalore
A Hybrid Approach to Scalable and Robust Spoken Language Understanding in Enterprise Virtual Agents (2021.naacl-industry)
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Ryan Price, Mahnoosh Mehrabani, Narendra Gupta, Yeon-Jun Kim, Shahab Jalalvand, Minhua Chen, Yanjie Zhao, Srinivas Bangalore
| Challenge: | Spoken language understanding (SLU) extracts the intended mean- ing from a user's utterance. |
| Approach: | They propose a framework for intent and entity extraction utilizing a hybrid of statistical and rule-based approaches. |
| Outcome: | The proposed framework can be deployed quickly for a large class of EVA applications with little need for human intervention. |
Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers (2020.findings-emnlp)
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| Challenge: | Named entity recognition models use a conditional random field as the final layer . current work eschews prior knowledge of how the span encoding scheme works . |
| Approach: | They propose to constrain the output to suppress illegal transitions to train a tagger with a cross-entropy loss twice as fast as a CRF. |
| Outcome: | The proposed model trains twice as fast as a CRF with statistically insignificant differences in F1 . the proposed model is open source and can be used in PyTorch and TensorFlow. |
Intent Features for Rich Natural Language Understanding (2021.naacl-industry)
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| Challenge: | generic dialog systems, or chatbots, are increasingly popular, but most industrial dialog systems are built for specific clients and use cases. |
| Approach: | They propose a new neural network architecture that allows for domain and topic agnostic properties of intents that can be learnt from syntactic cues only. |
| Outcome: | The proposed model improves on baselines for identifying intent features in a deployed, multi-intent natural language understanding module. |
E2E Spoken Entity Extraction for Virtual Agents (2023.emnlp-industry)
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| Challenge: | Extensive research has been done to recognize entities in spoken input. |
| Approach: | They propose to fine-tune pre-trained speech encoders to extract spoken entities directly from speech without the need for text transcription. |
| Outcome: | The proposed approach outperforms the 2-step approach for extracting spoken entities from human-computer conversations. |