Enhancing open-domain dialogue answer selection through Intent-Calibrated Self-Training
Can predicted intents calibrate correct answers in open-domain dialogues? The capability of predicted intents to refine answer selection in open-domain dialogues is a topic of significant interest. The mission of VOXReality is to explore the development of advanced context-aware task-oriented dialogue systems. In this context, Centrum Wiskunde & Informatica (CWI) has extensively explored and provided insights into whether predicted intent labels have the potential to calibrate answer labels in open-domain dialogues. Spearheaded by the Distributions and Interactive Systems (DIS) group, this initiative has culminated in the publication of a paper titled “Intent-Calibrated Self-Training for Answer Selection in Open-domain Dialogues” on Transactions of the Association for Computational Linguistics (TACL). This publication serves as an evidence of the significant progress made in understanding the intricate interplay between predicted intent labels and calibrated answer selection.
|Distributed and Interactive Systems
Pei, J. (2023). Enhancing open-domain dialogue answer selection through Intent-Calibrated Self-Training.