基于实例加权的互补最后话语选择多轮反应选择模型的改进
Improving Multi-Turn Response Selection Models with Complementary Last-Utterance Selection by Instance Weighting
摘要：
基于开放域检索的对话系统需要大量的训练数据来学习它们的参数。然而，在实践中，训练数据的负样本通常是从未注释的会话数据集中随机选择的。生成的训练数据可能包含噪声并影响响应选择模型的性能。为了解决这个困难，我们考虑利用数据资源本身的潜在相关性来获得不同类型的监督信号，并减少噪声数据的影响。更特别的是，我们考虑一个主要的互补任务对。主任务（\ie，我们的焦点）根据最后一句话和上下文选择正确的回答，补充任务根据回答和上下文选择最后一句话。关键点在于，补充任务的输出用于设置主任务的实例权重。我们在两个公共数据集上进行了广泛的实验，并在两个数据集上都获得了显着的改善。我们还从多个方面对该方法的变体进行了研究，结果验证了该方法的有效性。
英文摘要：
Open-domain retrieval-based dialogue systems require a considerable amount of training data to learn their parameters. However, in practice, the negative samples of training data are usually selected from an unannotated conversation data set at random. The generated training data is likely to contain noise and affect the performance of the response selection models. To address this difficulty, we consider utilizing the underlying correlation in the data resource itself to derive different kinds of supervision signals and reduce the influence of noisy data. More specially, we consider a main-complementary task pair. The main task (\ie our focus) selects the correct response given the last utterance and context, and the complementary task selects the last utterance given the response and context. The key point is that the output of the complementary task is used to set instance weights for the main task. We conduct extensive experiments in two public datasets and obtain significant improvement in both datasets. We also investigate the variant of our approach in multiple aspects, and the results have verified the effectiveness of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
