G
Genta Indra Winata
Researcher at Hong Kong University of Science and Technology
Publications - 79
Citations - 1649
Genta Indra Winata is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Language model & Computer science. The author has an hindex of 18, co-authored 79 publications receiving 964 citations. Previous affiliations of Genta Indra Winata include Salesforce.com & Bandung Institute of Technology.
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Proceedings ArticleDOI
MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems
TL;DR: This paper introduces Levenshtein belief spans (Lev), that allows efficient dialogue state tracking with a minimal generation length, and greatly improves the inference efficiency of MinTL-based systems.
Posted Content
IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding
Bryan Wilie,Karissa Vincentio,Genta Indra Winata,Samuel Cahyawijaya,Xiaohong Li,Zhi Yuan Lim,Sidik Soleman,Rahmad Mahendra,Pascale Fung,Syafri Bahar,Ayu Purwarianti +10 more
TL;DR: The first-ever vast resource for training, evaluation, and benchmarking on Indonesian natural language understanding (IndoNLU) tasks is introduced, releasing baseline models for all twelve tasks, as well as the framework for benchmark evaluation, thus enabling everyone to benchmark their system performances.
Journal ArticleDOI
Caire: An end-to-end empathetic chatbot
Zhaojiang Lin,Peng Xu,Genta Indra Winata,Farhad Bin Siddique,Zihan Liu,Jamin Shin,Pascale Fung +6 more
TL;DR: CAiRE as discussed by the authors is an end-to-end generative empathetic chatbot designed to recognize user emotions and respond in an empathic manner via transfer learning, which is built primarily to focus on empathy integration in fully data-driven generative dialogue systems.
Journal ArticleDOI
Attention-Informed Mixed-Language Training for Zero-Shot Cross-Lingual Task-Oriented Dialogue Systems
TL;DR: Attention-Informed Mixed-Language Training (MLT) is introduced, a novel zero-shot adaptation method for cross-lingual task-oriented dialogue systems that leverages very few task-related parallel word pairs to generate code-switching sentences for learning the inter-lingUAL semantics across languages.
Proceedings ArticleDOI
Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables
TL;DR: The authors proposed a zero-shot adaptation of task-oriented dialogue system to low-resource languages by using a set of very few parallel word pairs to refine the aligned cross-lingual word-level representations and employed a latent variable model to cope with the variance of similar sentences across different languages.