158 citations · 593 across the 50 of their papers we have counts for
8 papers · 1 filter
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking
Jian-Guo Zhang, Kazuma Hashimoto, Chien-Sheng Wu +4
Dialog state tracking (DST) is a core component in task-oriented dialog systems. Existing approaches for DST mainly fall into one of two categories, namely, ontology-based and onto…
Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences
Genta Indra Winata, Andrea Madotto, Chien-Sheng Wu +1
Training code-switched language models is difficult due to lack of data and complexity in the grammatical structure. Linguistic constraint theories have been used for decades to ge…
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning
Peng Xu, Chien-Sheng Wu, Andrea Madotto +1
Sensational headlines are headlines that capture people's attention and generate reader interest. Conventional abstractive headline generation methods, unlike human writers, do not…
Getting To Know You: User Attribute Extraction from Dialogues
Chien-Sheng Wu, Andrea Madotto, Zhaojiang Lin +2
User attributes provide rich and useful information for user understanding, yet structured and easy-to-use attributes are often sparsely populated. In this paper, we leverage dialo…
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
Chien-Sheng Wu, Andrea Madotto, Ehsan Hosseini-Asl +3
Over-dependence on domain ontology and lack of knowledge sharing across domains are two practical and yet less studied problems of dialogue state tracking. Existing approaches gene…
Personalizing Dialogue Agents via Meta-Learning
Zhaojiang Lin, Andrea Madotto, Chien-Sheng Wu +1
Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency. Collecting such descriptions from existing dialogues is expensive and…
Learning to Memorize in Neural Task-Oriented Dialogue Systems
Chien-Sheng Wu
In this thesis, we leverage the neural copy mechanism and memory-augmented neural networks (MANNs) to address existing challenge of neural task-oriented dialogue learning. We show…
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
Chien-Sheng Wu, Richard Socher, Caiming Xiong
End-to-end task-oriented dialogue is challenging since knowledge bases are usually large, dynamic and hard to incorporate into a learning framework. We propose the global-to-local…