565 citations · 1.1k across the 41 of their papers we have counts for
14 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.…
CrossNER: Evaluating Cross-Domain Named Entity Recognition
Zihan Liu, Yan Xu, Tiezheng Yu +5
Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains. However, most of the existing NER benchmarks lack doma…
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…
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…
Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems
Andrea Madotto, Zihan Liu, Zhaojiang Lin +1
Task-oriented dialogue systems use four connected modules, namely, Natural Language Understanding (NLU), a Dialogue State Tracking (DST), Dialogue Policy (DP) and Natural Language…
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…
Misinformation Has High Perplexity
Nayeon Lee, Yejin Bang, Andrea Madotto +1
Debunking misinformation is an important and time-critical task as there could be adverse consequences when misinformation is not quashed promptly. However, the usual supervised ap…
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 Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning
Zihan Liu, Genta Indra Winata, Andrea Madotto +1
Recently, fine-tuning pre-trained language models (e.g., multilingual BERT) to downstream cross-lingual tasks has shown promising results. However, the fine-tuning process inevitab…
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…
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…
Exploration Based Language Learning for Text-Based Games
Andrea Madotto, Mahdi Namazifar, Joost Huizinga +7
This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. Text-based computer games describ…
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…