46 citations · 231 across the 24 of their papers we have counts for
13 papers · 1 filter
Continual Learning in Task-Oriented Dialogue Systems
Andrea Madotto, Zhaojiang Lin, Zhenpeng Zhou +6
Continual learning in task-oriented dialogue systems can allow us to add new domains and functionalities through time without incurring the high cost of a whole system retraining.…
Plug-and-Play Conversational Models
Andrea Madotto, Etsuko Ishii, Zhaojiang Lin +2
There has been considerable progress made towards conversational models that generate coherent and fluent responses; however, this often involves training large language models on…
Cross-lingual Spoken Language Understanding with Regularized Representation Alignment
Zihan Liu, Genta Indra Winata, Peng Xu +2
Despite the promising results of current cross-lingual models for spoken language understanding systems, they still suffer from imperfect cross-lingual representation alignments be…
Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems
Andrea Madotto, Samuel Cahyawijaya, Genta Indra Winata +4
Task-oriented dialogue systems are either modularized with separate dialogue state tracking (DST) and management steps or end-to-end trainable. In either case, the knowledge base (…
MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems
Zhaojiang Lin, Andrea Madotto, Genta Indra Winata +1
In this paper, we propose Minimalist Transfer Learning (MinTL) to simplify the system design process of task-oriented dialogue systems and alleviate the over-dependency on annotate…
EmoGraph: Capturing Emotion Correlations using Graph Networks
Peng Xu, Zihan Liu, Genta Indra Winata +2
Most emotion recognition methods tackle the emotion understanding task by considering individual emotion independently while ignoring their fuzziness nature and the interconnection…
The Adapter-Bot: All-In-One Controllable Conversational Model
Andrea Madotto, Zhaojiang Lin, Yejin Bang +1
Considerable progress has been made towards conversational models that generate coherent and fluent responses by training large language models on large dialogue datasets. These mo…
Meta-Transfer Learning for Code-Switched Speech Recognition
Genta Indra Winata, Samuel Cahyawijaya, Zhaojiang Lin +3
An increasing number of people in the world today speak a mixed-language as a result of being multilingual. However, building a speech recognition system for code-switching remains…
XPersona: Evaluating Multilingual Personalized Chatbot
Zhaojiang Lin, Zihan Liu, Genta Indra Winata +5
Personalized dialogue systems are an essential step toward better human-machine interaction. Existing personalized dialogue agents rely on properly designed conversational datasets…
Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning
Zhaojiang Lin, Andrea Madotto, Pascale Fung
Fine-tuning pre-trained generative language models to down-stream language generation tasks has shown promising results. However, this comes with the cost of having a single, large…
Variational Transformers for Diverse Response Generation
Zhaojiang Lin, Genta Indra Winata, Peng Xu +2
Despite the great promise of Transformers in many sequence modeling tasks (e.g., machine translation), their deterministic nature hinders them from generalizing to high entropy tas…
Learning Fast Adaptation on Cross-Accented Speech Recognition
Genta Indra Winata, Samuel Cahyawijaya, Zihan Liu +4
Local dialects influence people to pronounce words of the same language differently from each other. The great variability and complex characteristics of accents creates a major ch…
Attention over Parameters for Dialogue Systems
Andrea Madotto, Zhaojiang Lin, Chien-Sheng Wu +2
Dialogue systems require a great deal of different but complementary expertise to assist, inform, and entertain humans. For example, different domains (e.g., restaurant reservation…