转移时间：预测和评估人机聊天障碍
Time to Transfer: Predicting and Evaluating Machine-Human Chatting Handoff
摘要：
chatbot能完全取代人类代理吗？简短的回答可能是-“这取决于... ".对于一些具有挑战性的情况，例如，对话的主题频谱超出训练语料库覆盖范围，聊天机器人可能会发生故障并返回不满意的话语。这个问题可以通过引入人机聊天机器人（MHCH）来解决，它可以实现人类算法的协作。为了检测正常/可转移的话语，我们提出了一个困难辅助匹配推理（DAMI）网络，利用困难辅助编码，以提高话语的表示。此外，匹配推理机制被引入到捕获上下文匹配功能。通过考虑MHCH的容差特性，提出了一种新的性能评价指标-容差内黄金传输（GT-T）。为了深入了解任务并验证所提出的模型，我们收集了两个新的数据集。广泛的实验结果，并与一系列的基线模型进行对比，以证明我们的模型对MHCH的疗效。
英文摘要：
Is chatbot able to completely replace the human agent? The short answer could be - "it depends...". For some challenging cases, e.g., dialogue's topical spectrum spreads beyond the training corpus coverage, the chatbot may malfunction and return unsatisfied utterances. This problem can be addressed by introducing the Machine-Human Chatting Handoff (MHCH), which enables human-algorithm collaboration. To detect the normal/transferable utterances, we propose a Difficulty-Assisted Matching Inference (DAMI) network, utilizing difficulty-assisted encoding to enhance the representations of utterances. Moreover, a matching inference mechanism is introduced to capture the contextual matching features. A new evaluation metric, Golden Transfer within Tolerance (GT-T), is proposed to assess the performance by considering the tolerance property of the MHCH. To provide insights into the task and validate the proposed model, we collect two new datasets. Extensive experimental results are presented and contrasted against a series of baseline models to demonstrate the efficacy of our model on MHCH.
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。
