Priyanka Agrawal


2022

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Proceedings of the Sixth Workshop on Structured Prediction for NLP
Andreas Vlachos | Priyanka Agrawal | André Martins | Gerasimos Lampouras | Chunchuan Lyu
Proceedings of the Sixth Workshop on Structured Prediction for NLP

2021

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Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)
Zornitsa Kozareva | Sujith Ravi | Andreas Vlachos | Priyanka Agrawal | André Martins
Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)

2020

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Proceedings of the Fourth Workshop on Structured Prediction for NLP
Priyanka Agrawal | Zornitsa Kozareva | Julia Kreutzer | Gerasimos Lampouras | André Martins | Sujith Ravi | Andreas Vlachos
Proceedings of the Fourth Workshop on Structured Prediction for NLP

2019

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Unified Semantic Parsing with Weak Supervision
Priyanka Agrawal | Ayushi Dalmia | Parag Jain | Abhishek Bansal | Ashish Mittal | Karthik Sankaranarayanan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Semantic parsing over multiple knowledge bases enables a parser to exploit structural similarities of programs across the multiple domains. However, the fundamental challenge lies in obtaining high-quality annotations of (utterance, program) pairs across various domains needed for training such models. To overcome this, we propose a novel framework to build a unified multi-domain enabled semantic parser trained only with weak supervision (denotations). Weakly supervised training is particularly arduous as the program search space grows exponentially in a multi-domain setting. To solve this, we incorporate a multi-policy distillation mechanism in which we first train domain-specific semantic parsers (teachers) using weak supervision in the absence of the ground truth programs, followed by training a single unified parser (student) from the domain specific policies obtained from these teachers. The resultant semantic parser is not only compact but also generalizes better, and generates more accurate programs. It further does not require the user to provide a domain label while querying. On the standard Overnight dataset (containing multiple domains), we demonstrate that the proposed model improves performance by 20% in terms of denotation accuracy in comparison to baseline techniques.

2012

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Proceedings of the Workshop on Question Answering for Complex Domains
Nanda Kambhatla | Sachindra Joshi | Ganesh Ramakrishnan | Kiran Kate | Priyanka Agrawal
Proceedings of the Workshop on Question Answering for Complex Domains