基于Hawkes过程的对话中用户满意度动态建模
Modeling User Satisfaction Dynamics in Dialogue via Hawkes Process
AI总结：
针对对话评估中现有用户满意度估计器忽略跨轮次满意度动态的问题，提出基于Hawkes过程的ASAP估计器，将满意度建模为事件序列，在四个基准数据集上显著优于基线方法。
摘要：
对话系统受到越来越多的关注，但自动评估其性能仍然具有挑战性。用户满意度估计（USE）被提出作为一种替代方案。它假设对话系统的性能可以通过用户满意度来衡量，并使用一个估计器来模拟用户。USE的有效性在很大程度上取决于该估计器。现有的估计器独立地预测每一轮的用户满意度，而忽略了对话中各轮之间的满意度动态。为了充分模拟用户，考虑满意度动态至关重要。为了填补这一空白，我们提出了一种新的估计器ASAP（sAtisfaction eStimation via HAwkes Process，基于Hawkes过程的满意度估计），它将各轮的用户满意度视为一个事件序列，并采用Hawkes过程来有效地建模该序列中的动态。在四个基准对话数据集上的实验结果表明，ASAP可以大幅优于最先进的基线估计器。
英文摘要：
Dialogue systems have received increasing attention while automatically evaluating their performance remains challenging. User satisfaction estimation (USE) has been proposed as an alternative. It assumes that the performance of a dialogue system can be measured by user satisfaction and uses an estimator to simulate users. The effectiveness of USE depends heavily on the estimator. Existing estimators independently predict user satisfaction at each turn and ignore satisfaction dynamics across turns within a dialogue. In order to fully simulate users, it is crucial to take satisfaction dynamics into account. To fill this gap, we propose a new estimator ASAP (sAtisfaction eStimation via HAwkes Process) that treats user satisfaction across turns as an event sequence and employs a Hawkes process to effectively model the dynamics in this sequence. Experimental results on four benchmark dialogue datasets demonstrate that ASAP can substantially outperform state-of-the-art baseline estimators.
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。
