Context-Aware Dialog Re-Ranking for Task-Oriented Dialog Systems
This work addresses a gap in task-oriented dialog systems by enhancing response ranking to handle speech recognition errors, though it is incremental as it builds on existing methods.
The paper tackles the problem of improving task-oriented dialog systems by proposing a context-aware dialog response re-ranking system that combines matching scores with existing system probabilities, resulting in improved performance on real dialogs with speech recognition errors.
Dialog response ranking is used to rank response candidates by considering their relation to the dialog history. Although researchers have addressed this concept for open-domain dialogs, little attention has been focused on task-oriented dialogs. Furthermore, no previous studies have analyzed whether response ranking can improve the performance of existing dialog systems in real human-computer dialogs with speech recognition errors. In this paper, we propose a context-aware dialog response re-ranking system. Our system reranks responses in two steps: (1) it calculates matching scores for each candidate response and the current dialog context; (2) it combines the matching scores and a probability distribution of the candidates from an existing dialog system for response re-ranking. By using neural word embedding-based models and handcrafted or logistic regression-based ensemble models, we have improved the performance of a recently proposed end-to-end task-oriented dialog system on real dialogs with speech recognition errors.