activity
20192022
most citedLearning from Rules Generalizing Labeled Exemplars

22 citations · 23 across the 4 of their papers we have counts for

collaborators

8 papers

cs.CL20221 cited

Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers

Abhijeet Awasthi, Ashutosh Sathe, Sunita Sarawagi

Text-to-SQL parsers typically struggle with databases unseen during the train time. Adapting parsers to new databases is a challenging problem due to the lack of natural language q…

cs.CL2021

Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study

Yash Khemchandani, Sarvesh Mehtani, Vaidehi Patil +3

Recent research in multilingual language models (LM) has demonstrated their ability to effectively handle multiple languages in a single model. This holds promise for low web-resou…

eess.AS2021

Teaching keyword spotters to spot new keywords with limited examples

Abhijeet Awasthi, Kevin Kilgour, Hassan Rom

Learning to recognize new keywords with just a few examples is essential for personalizing keyword spotting (KWS) models to a user's choice of keywords. However, modern KWS models…

cs.SD2021

Error-driven Fixed-Budget ASR Personalization for Accented Speakers

Abhijeet Awasthi, Aman Kansal, Sunita Sarawagi +1

We consider the task of personalizing ASR models while being constrained by a fixed budget on recording speaker-specific utterances. Given a speaker and an ASR model, we propose a…

cs.CL2020

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

Sriram Balasubramanian, Naman Jain, Gaurav Jindal +2

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

eess.AS2020

Black-box Adaptation of ASR for Accented Speech

Kartik Khandelwal, Preethi Jyothi, Abhijeet Awasthi +1

We introduce the problem of adapting a black-box, cloud-based ASR system to speech from a target accent. While leading online ASR services obtain impressive performance on main-str…