TY - GEN
T1 - Post-Abstention
T2 - 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023
AU - Varshney, Neeraj
AU - Baral, Chitta
N1 - Publisher Copyright:
© 2023 Association for Computational Linguistics.
PY - 2023
Y1 - 2023
N2 - Despite remarkable progress made in natural language processing, even the state-of-the-art models often make incorrect predictions. Such predictions hamper the reliability of systems and limit their widespread adoption in real-world applications. Selective prediction partly addresses the above concern by enabling models to abstain from answering when their predictions are likely to be incorrect. While selective prediction is advantageous, it leaves us with a pertinent question 'what to do after abstention'. To this end, we present an explorative study on 'Post-Abstention', a task that allows re-attempting the abstained instances with the aim of increasing coverage of the system without significantly sacrificing its accuracy. We first provide mathematical formulation of this task and then explore several methods to solve it. Comprehensive experiments on 11 QA datasets show that these methods lead to considerable risk improvements -performance metric of the Post-Abstention task- both in the in-domain and the out-of-domain settings. We also conduct a thorough analysis of these results which further leads to several interesting findings. Finally, we believe that our work will encourage and facilitate further research in this important area of addressing the reliability of NLP systems.
AB - Despite remarkable progress made in natural language processing, even the state-of-the-art models often make incorrect predictions. Such predictions hamper the reliability of systems and limit their widespread adoption in real-world applications. Selective prediction partly addresses the above concern by enabling models to abstain from answering when their predictions are likely to be incorrect. While selective prediction is advantageous, it leaves us with a pertinent question 'what to do after abstention'. To this end, we present an explorative study on 'Post-Abstention', a task that allows re-attempting the abstained instances with the aim of increasing coverage of the system without significantly sacrificing its accuracy. We first provide mathematical formulation of this task and then explore several methods to solve it. Comprehensive experiments on 11 QA datasets show that these methods lead to considerable risk improvements -performance metric of the Post-Abstention task- both in the in-domain and the out-of-domain settings. We also conduct a thorough analysis of these results which further leads to several interesting findings. Finally, we believe that our work will encourage and facilitate further research in this important area of addressing the reliability of NLP systems.
UR - http://www.scopus.com/inward/record.url?scp=85174418659&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174418659&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85174418659
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 967
EP - 982
BT - Long Papers
PB - Association for Computational Linguistics (ACL)
Y2 - 9 July 2023 through 14 July 2023
ER -