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.

翻译：开放域对话中的答案选择旨在从候选项中选出准确的答案。近期答案选择模型的成功依赖于使用大量标注数据进行训练。然而，收集大规模标注数据既费时又费力。本文提出在自训练范式中引入预测的意图标签来校准答案标签。具体而言，我们提出意图校准自训练方法（ICAST），通过意图校准的答案选择范式提升伪答案标签的质量，其中利用伪意图标签辅助改进伪答案标签。我们在两个开放域对话基准数据集上开展了大量实验。实验结果表明，ICAST在使用1%、5%和10%标注数据时均能持续优于基线方法。特别地，与仅使用5%标注数据的最强基线相比，该方法在两个数据集上的F1分数分别提升了2.06%和1.00%。