565 citations · 1.1k across the 41 of their papers we have counts for
11 papers · 1 filter
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Sumanth Dathathri, Andrea Madotto, Janice Lan +5
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated languag…
Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables
Zihan Liu, Jamin Shin, Yan Xu +4
Despite the surging demands for multilingual task-oriented dialog systems (e.g., Alexa, Google Home), there has been less research done in multilingual or cross-lingual scenarios.…
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…
On the Effectiveness of Low-Rank Matrix Factorization for LSTM Model Compression
Genta Indra Winata, Andrea Madotto, Jamin Shin +2
Despite their ubiquity in NLP tasks, Long Short-Term Memory (LSTM) networks suffer from computational inefficiencies caused by inherent unparallelizable recurrences, which further…
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_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…
Generating Empathetic Responses by Looking Ahead the User's Sentiment
Jamin Shin, Peng Xu, Andrea Madotto +1
An important aspect of human conversation difficult for machines is conversing with empathy, which is to understand the user's emotion and respond appropriately. Recent neural conv…
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…