  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.
