论文标题
对话响应选择分层课程学习
Dialogue Response Selection with Hierarchical Curriculum Learning
论文作者
论文摘要
我们研究对话响应选择的匹配模型的学习。在最近的发现中，在现实情况下，接受随机阴性样本训练的模型并不理想，我们提出了一个分层课程学习框架，该框架在“易于缺乏的”方案中训练匹配模型。我们的学习框架由两个补充课程组成：（1）语料库级课程（CC）； （2）实例级课程（IC）。在CC中，该模型逐渐提高了其在对话环境和响应候选者之间找到匹配线索的能力。至于IC，它逐渐增强了模型在识别对话环境和响应候选者之间的不匹配信息的能力。具有三个最先进的匹配模型的三个基准数据集的实证研究表明，所提出的学习框架可显着改善各种评估指标的模型性能。
We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios, we propose a hierarchical curriculum learning framework that trains the matching model in an "easy-to-difficult" scheme. Our learning framework consists of two complementary curricula: (1) corpus-level curriculum (CC); and (2) instance-level curriculum (IC). In CC, the model gradually increases its ability in finding the matching clues between the dialogue context and a response candidate. As for IC, it progressively strengthens the model's ability in identifying the mismatching information between the dialogue context and a response candidate. Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics.
