2 citations · 3 across the 5 of their papers we have counts for
8 papers
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…
Multi-Stage Network Embedding for Exploring Heterogeneous Edges
Hong Huang, Yu Song, Fanghua Ye +3
The relationships between objects in a network are typically diverse and complex, leading to the heterogeneous edges with different semantic information. In this paper, we focus on…
Modeling Heterogeneous Edges to Represent Networks with Graph Auto-Encoder
Lu Wang, Yu Song, Hong Huang +3
In the real world, networks often contain multiple relationships among nodes, manifested as the heterogeneity of the edges in the networks. We convert the heterogeneous networks in…
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…