1 code implementation • 8 Mar 2023 • Philipp Ennen, Po-chun Hsu, Chan-Jan Hsu, Chang-Le Liu, Yen-chen Wu, Yin-Hsiang Liao, Chin-Tung Lin, Da-Shan Shiu, Wei-Yun Ma
In this paper we present the multilingual language model BLOOM-zh that features enhanced support for Traditional Chinese.
no code implementations • NAACL 2021 • Yen-chen Wu, Carl Edward Rasmussen
Second, in advantage clipping, we estimate and clip the advantages of useless responses and normal ones separately.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Yen-chen Wu, Bo-Hsiang Tseng, Milica Gasic
In order to improve the sample-efficiency of deep reinforcement learning (DRL), we implemented imagination augmented agent (I2A) in spoken dialogue systems (SDS).
no code implementations • WS 2019 • Bo-Hsiang Tseng, Paweł Budzianowski, Yen-chen Wu, Milica Gašić
Domain adaptation in natural language generation (NLG) remains challenging because of the high complexity of input semantics across domains and limited data of a target domain.
no code implementations • WS 2018 • Stefan Ultes, Paweł\ Budzianowski, Iñigo Casanueva, Lina Rojas-Barahona, Bo-Hsiang Tseng, Yen-chen Wu, Steve Young, Milica Gašić
Statistical spoken dialogue systems usually rely on a single- or multi-domain dialogue model that is restricted in its capabilities of modelling complex dialogue structures, e. g., relations.
1 code implementation • WS 2018 • Bo-Hsiang Tseng, Florian Kreyssig, Pawel Budzianowski, Inigo Casanueva, Yen-chen Wu, Stefan Ultes, Milica Gasic
Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling.
no code implementations • WS 2018 • I{\~n}igo Casanueva, Pawe{\l} Budzianowski, Stefan Ultes, Florian Kreyssig, Bo-Hsiang Tseng, Yen-chen Wu, Milica Ga{\v{s}}i{\'c}
Reinforcement learning (RL) is a promising dialogue policy optimisation approach, but traditional RL algorithms fail to scale to large domains.
no code implementations • 16 Sep 2016 • Yen-chen Wu, Tzu-Hsiang Lin, Yang-De Chen, Hung-Yi Lee, Lin-shan Lee
In our previous work, some hand-crafted states estimated from the present retrieval results are used to determine the proper actions.
no code implementations • 7 Jun 2015 • Cheng-Tao Chung, Cheng-Yu Tsai, Hsiang-Hung Lu, Yuan-ming Liou, Yen-chen Wu, Yen-Ju Lu, Hung-Yi Lee, Lin-shan Lee
The Multi-layered Acoustic Tokenizer (MAT) proposed in this work automatically discovers multiple sets of acoustic tokens from the given corpus.