Answer selection in open-domain dialogues aims to select an accurate answer from candidates. Recent success of answer selection models hinges on training with large amounts of labeled data. However, collecting large-scale labeled data is labor-intensive and time-consuming. In this paper, we introduce the predicted intent labels to calibrate answer labels in a self-training paradigm. Specifically, we propose the intent-calibrated self-training (ICAST) to improve the quality of pseudo answer labels through the intent-calibrated answer selection paradigm, in which we employ pseudo intent labels to help improve pseudo answer labels. We carry out extensive experiments on two benchmark datasets with open-domain dialogues. The experimental results show that ICAST outperforms baselines consistently with 1%, 5% and 10% labeled data. Specifically, it improves 2.06% and 1.00% of F1 score on the two datasets, compared with the strongest baseline with only 5% labeled data.
@article{arxiv.2307.06703,
title = {Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues},
author = {Wentao Deng and Jiahuan Pei and Zhaochun Ren and Zhumin Chen and Pengjie Ren},
journal= {arXiv preprint arXiv:2307.06703},
year = {2023}
}
Comments
This arXiv version is a pre-MIT Press publication version, this paper has been accepted by TACL. 16 pages, 3 figures, 4 tables