文件名称：End-to-End Task-Completion Neural Dialogue Systems
文件大小：892KB
文件格式：PDF
更新时间：2021-09-30 02:39:16
NLP Deep Learing End-to-End Dialogue
One of the major drawbacks of modu- larized task-completion dialogue systems is that each module is trained individu- ally, which presents several challenges. For example, downstream modules are af- fected by earlier modules, and the per- formance of the entire system is not ro- bust to the accumulated errors. This pa- per presents a novel end-to-end learning framework for task-completion dialogue systems to tackle such issues. Our neu- ral dialogue system can directly interact with a structured database to assist users in accessing information and accomplish- ing certain tasks. The reinforcement learn- ing based dialogue manager offers robust capabilities to handle noises caused by other components of the dialogue system. Our experiments in a movie-ticket book- ing domain show that our end-to-end sys- tem not only outperforms modularized di- alogue system baselines for both objective and subjective evaluation, but also is ro- bust to noises as demonstrated by several systematic experiments with different er- ror granularity and rates specific to the lan- guage understanding module1.