Dialog state tracking is used to estimate the current belief state of a dialog given all the preceding conversation. Machine reading comprehension, on the other hand, focuses on building systems that read passages of text and answer questions that require some understanding of passages. We formulate dialog state tracking as a reading comprehension task to answer the question $what\ is\ the\ state\ of\ the\ current\ dialog?$ after reading conversational context. In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation. Experiments on MultiWOZ-2.0 cross-domain dialog dataset show that our simple system can obtain similar accuracies compared to the previous more complex methods. By exploiting recent advances in contextual word embeddings, adding a model that explicitly tracks whether a slot value should be carried over to the next turn, and combining our method with a traditional joint state tracking method that relies on closed set vocabulary, we can obtain a joint-goal accuracy of $47.33\%$ on the standard test split, exceeding current state-of-the-art by $11.75\%$**.

本研究通过利用注意力机制为基础的神经网络，将对话状态跟踪任务转化为阅读理解问答，利用上下文意义理解对话中不同语境中槽位词语的指代，通过利用上下文单词嵌入的最新进展，并将方法与传统的联合状态跟踪方法相结合，得到了比当前最新技术提高11.75%的检测精度。

对话状态跟踪：一种神经阅读理解方法