Search Results for author: Abhijeet Awasthi

Found 10 papers, 5 papers with code

Structured Case-based Reasoning for Inference-time Adaptation of Text-to-SQL parsers

no code implementations10 Jan 2023 Abhijeet Awasthi, Soumen Chakrabarti, Sunita Sarawagi

To the best of our knowledge, we are the first to attempt inference-time adaptation of Text-to-SQL models, and harness trainable structured similarity between subqueries.

Semantic Parsing Text-To-SQL

Bootstrapping Multilingual Semantic Parsers using Large Language Models

no code implementations13 Oct 2022 Abhijeet Awasthi, Nitish Gupta, Bidisha Samanta, Shachi Dave, Sunita Sarawagi, Partha Talukdar

Despite cross-lingual generalization demonstrated by pre-trained multilingual models, the translate-train paradigm of transferring English datasets across multiple languages remains to be a key mechanism for training task-specific multilingual models.

Semantic Parsing Translation

Teaching keyword spotters to spot new keywords with limited examples

no code implementations4 Jun 2021 Abhijeet Awasthi, Kevin Kilgour, Hassan Rom

Towards easily customizable KWS models, we present KeySEM (Keyword Speech EMbedding), a speech embedding model pre-trained on the task of recognizing a large number of keywords.

Keyword Spotting

Error-driven Fixed-Budget ASR Personalization for Accented Speakers

1 code implementation4 Mar 2021 Abhijeet Awasthi, Aman Kansal, Sunita Sarawagi, Preethi Jyothi

We consider the task of personalizing ASR models while being constrained by a fixed budget on recording speaker-specific utterances.

What's in a Name? Are BERT Named Entity Representations just as Good for any other Name?

no code implementations WS 2020 Sriram Balasubramanian, Naman jain, Gaurav Jindal, Abhijeet Awasthi, Sunita Sarawagi

We evaluate named entity representations of BERT-based NLP models by investigating their robustness to replacements from the same typed class in the input.

Black-box Adaptation of ASR for Accented Speech

1 code implementation24 Jun 2020 Kartik Khandelwal, Preethi Jyothi, Abhijeet Awasthi, Sunita Sarawagi

Accordingly, we propose a novel coupling of an open-source accent-tuned local model with the black-box service where the output from the service guides frame-level inference in the local model.

Learning from Rules Generalizing Labeled Exemplars

2 code implementations ICLR 2020 Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, Sunita Sarawagi

Empirical evaluation on five different tasks shows that (1) our algorithm is more accurate than several existing methods of learning from a mix of clean and noisy supervision, and (2) the coupled rule-exemplar supervision is effective in denoising rules.


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