35 citations · 43 across the 10 of their papers we have counts for
6 papers · 1 filter
Modeling User Satisfaction Dynamics in Dialogue via Hawkes Process
Fanghua Ye, Zhiyuan Hu, Emine Yilmaz
Dialogue systems have received increasing attention while automatically evaluating their performance remains challenging. User satisfaction estimation (USE) has been proposed as an…
MetaASSIST: Robust Dialogue State Tracking with Meta Learning
Fanghua Ye, Xi Wang, Jie Huang +3
Existing dialogue datasets contain lots of noise in their state annotations. Such noise can hurt model training and ultimately lead to poor generalization performance. A general fr…
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking
Yue Feng, Aldo Lipani, Fanghua Ye +2
Dialogue State Tracking (DST) aims to keep track of users' intentions during the course of a conversation. In DST, modelling the relations among domains and slots is still an under…
ASSIST: Towards Label Noise-Robust Dialogue State Tracking
Fanghua Ye, Yue Feng, Emine Yilmaz
The MultiWOZ 2.0 dataset has greatly boosted the research on dialogue state tracking (DST). However, substantial noise has been discovered in its state annotations. Such noise brin…
MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation
Fanghua Ye, Jarana Manotumruksa, Emine Yilmaz
The MultiWOZ 2.0 dataset has greatly stimulated the research of task-oriented dialogue systems. However, its state annotations contain substantial noise, which hinders a proper eva…
Slot Self-Attentive Dialogue State Tracking
Fanghua Ye, Jarana Manotumruksa, Qiang Zhang +2
An indispensable component in task-oriented dialogue systems is the dialogue state tracker, which keeps track of users' intentions in the course of conversation. The typical approa…