46 citations · 231 across the 24 of their papers we have counts for
8 papers · 1 filter
Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems
Zihan Liu, Genta Indra Winata, Zhaojiang Lin +2
Recently, data-driven task-oriented dialogue systems have achieved promising performance in English. However, developing dialogue systems that support low-resource languages remain…
Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank Transformer
Genta Indra Winata, Samuel Cahyawijaya, Zhaojiang Lin +2
Highly performing deep neural networks come at the cost of computational complexity that limits their practicality for deployment on portable devices. We propose the low-rank trans…
Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition
Genta Indra Winata, Zhaojiang Lin, Jamin Shin +2
In countries that speak multiple main languages, mixing up different languages within a conversation is commonly called code-switching. Previous works addressing this challenge mai…
MoEL: Mixture of Empathetic Listeners
Zhaojiang Lin, Andrea Madotto, Jamin Shin +2
Previous research on empathetic dialogue systems has mostly focused on generating responses given certain emotions. However, being empathetic not only requires the ability of gener…
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…
CAiRE: An Empathetic Neural Chatbot
Zhaojiang Lin, Peng Xu, Genta Indra Winata +4
In this paper, we present an end-to-end empathetic conversation agent CAiRE. Our system adapts TransferTransfo (Wolf et al., 2019) learning approach that fine-tunes a large-scale p…
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification
Genta Indra Winata, Andrea Madotto, Zhaojiang Lin +4
Detecting emotion from dialogue is a challenge that has not yet been extensively surveyed. One could consider the emotion of each dialogue turn to be independent, but in this paper…
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…