Survival text regression for time-to-event prediction in conversations
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Authors
De Kock, C
Vlachos, A
Publication Date
2021-01-01Journal Title
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
ISBN
9781954085541
Pages
1219-1229
Type
Conference Object
This Version
AM
Metadata
Show full item recordCitation
De Kock, C., & Vlachos, A. (2021). Survival text regression for time-to-event prediction in conversations. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 1219-1229. https://doi.org/10.17863/CAM.83270
Abstract
Time-to-event prediction tasks are common in conversation modelling, for applications such as predicting the length of a conversation or when a user will stop contributing to a platform. Despite the fact that it is natural to frame such predictions as regression tasks, recent work has modelled them as classification tasks, determining whether the time-to-event is greater than a pre-determined cut-off point. While this allows for the application of classification models which are well studied in NLP, it imposes a formulation that is contrived, as well as less informative. In this paper, we explore how to handle time-to-event forecasting in conversations as regression tasks. We focus on a family of regression techniques known as survival regression, which are commonly used in the context of healthcare and reliability engineering. We adapt these models to time-to-event prediction in conversations, using linguistic markers as features. On three datasets, we demonstrate that they outperform commonly considered text regression methods and comparable classification models.
Sponsorship
EPSRC (EP/T023414/1)
Identifiers
External DOI: https://doi.org/10.17863/CAM.83270
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335837
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