一种基于动态随机状态演化的对话系统概率表示框架及基于熵的对话管理
A Probabilistic Framework for Representing Dialog Systems and Entropy-Based Dialog Management through Dynamic Stochastic State Evolution
摘要：
在本文中，我们提出了一个目标驱动的口语对话系统的概率框架。一个新的动态随机状态（DS-状态），然后定义一个对话状态在不同阶段的对话过程中的目标集的特征。此外，还提出了一种熵最小化对话管理（EMDM）策略，结合DS状态，以促进一个强大的和有效的解决方案，在达到用户的目标。一个歌曲点播任务，共38117首歌曲和12个属性对应于每首歌曲，被用来测试所提出的方法的性能。在理想的模拟中，假设没有错误，EMDM策略是所有测试方法中最有效的目标搜索方法，平均在3.3个对话圈内返回正确的歌曲。此外，在实际场景中，使用前五名候选人来处理不可避免的自动语音识别（ASR）和自然语言理解（NLU）错误，结果表明，当系统询问随机问题时，平均在6.23个对话回合中只能成功获得61.7%的对话目标，而如果使用在每个DS状态处执行的所提出的EMDM策略，利用来自SLU输出的前5个候选来更新所提出的DS状态，则可以在平均5.17个对话回合内有效地实现86.7%的对话成功率。我们还证明了基于熵的DM策略比非基于熵的DM更有效。此外，在EMDM中使用目标集分布，结果优于没有它们的结果，例如在最先进的数据库摘要DM中。
英文摘要：
In this paper, we present a probabilistic framework for goal-driven spoken dialog systems. A new dynamic stochastic state (DS-state) is then defined to characterize the goal set of a dialog state at different stages of the dialog process. Furthermore, an entropy minimization dialog management(EMDM) strategy is also proposed to combine with the DS-states to facilitate a robust and efficient solution in reaching a user's goals. A Song-On-Demand task, with a total of 38117 songs and 12 attributes corresponding to each song, is used to test the performance of the proposed approach. In an ideal simulation, assuming no errors, the EMDM strategy is the most efficient goal-seeking method among all tested approaches, returning the correct song within 3.3 dialog turns on average. Furthermore, in a practical scenario, with top five candidates to handle the unavoidable automatic speech recognition (ASR) and natural language understanding (NLU) errors, the results show that only 61.7\% of the dialog goals can be successfully obtained in 6.23 dialog turns on average when random questions are asked by the system, whereas if the proposed DS-states are updated with the top 5 candidates from the SLU output using the proposed EMDM strategy executed at every DS-state, then a 86.7\% dialog success rate can be accomplished effectively within 5.17 dialog turns on average. We also demonstrate that entropy-based DM strategies are more efficient than non-entropy based DM. Moreover, using the goal set distributions in EMDM, the results are better than those without them, such as in sate-of-the-art database summary DM.
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。
