With the increasing pervasiveness of smart phones and smart devices, dialogue systems are gaining ever growing attention from both academic and industry. These systems can be broadly classified into two categories, one that is aimed at helping user to gain new knowledge and one that can chat with users without completing any specific tasks. Although dialogue systems are improving substantially, the user experience of such systems are still unsatisfactory as there are no specific rules covering all possible situations of real human–machine dialogue, resulting in breakdowns. There are two technical issues affecting the detection of dialogue breakdown in an open domain conversation: human resources to prepare and annotate a large chunk of conversation data and dialogue histories containing words that don’t appear directly in training data. To tackle these issues, we propose a novel encoding method for temporal utterances with memory attention based on end-to-end dialogue breakdown detection. Specifically, long short-term memory (LSTM) is employed to encode each word of all previous user and system utterances. Encoded vectors from LSTM (user and system utterances), along with system and user utterances from sentence embedding, are then stored in memory wherein an attention mechanism is applied to select the most relevant piece of words from system and user utterances for breakdown detection. An evaluation of the proposed approach on a breakdown detection task (DBDC3) showed that the model for single-labeled breakdown detection outperforms other state-of-the-art methods in a classification task. In conclusion, a more effective knowledge gain and management can be achieved by integration of our proposed breakdown detection into dialogue systems.

中文翻译：

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将故障检测集成到对话系统中以改善知识管理：编码具有记忆力的时间话语

随着智能电话和智能设备的日益普及，对话系统越来越受到学术界和工业界的关注。这些系统可以大致分为两类，一类旨在帮助用户获得新知识，另一类可以与用户聊天而无需完成任何特定任务。尽管对话系统正在显着改善，但是由于没有具体规则涵盖真实的人机对话的所有可能情况，从而导致故障，此类系统的用户体验仍然不令人满意。在开放域对话中，有两个技术问题会影响对话崩溃的检测：准备和注释大量对话数据的人力资源以及对话历史记录中包含未直接出现在训练数据中的单词。为了解决这些问题，我们提出了一种新的编码方法，用于基于端到端对话故障检测的具有记忆注意力的时间话语。具体来说，采用长期短期记忆（LSTM）来编码所有先前用户和系统话语中的每个单词。然后，将LSTM（用户和系统话语）的编码矢量以及句子嵌入中的系统和用户话语存储在内存中，在内存中应用注意机制从系统和用户话语中选择最相关的单词以进行故障检测。对建议的故障检测任务方法（DBDC3）的评估表明，单标签故障检测模型优于分类任务中的其他最新方法。结论，