4 citations · 7 across the 8 of their papers we have counts for
8 papers
Cross-Tokenizer LLM Distillation through a Byte-Level Interface
Avyav Kumar Singh, Yen-Chen Wu, Alexandru Cioba +2
Cross-tokenizer distillation (CTD), the transfer of knowledge from a teacher to a student language model when the two use different tokenizers, remains a largely unsolved problem.…
Latent Flow Transformer
Yen-Chen Wu, Feng-Ting Liao, Meng-Hsi Chen +3
Transformers, the standard implementation for large language models (LLMs), typically consist of tens to hundreds of discrete layers. While more layers can lead to better performan…
Extending the Pre-Training of BLOOM for Improved Support of Traditional Chinese: Models, Methods and Results
Philipp Ennen, Po-Chun Hsu, Chan-Jan Hsu +6
In this paper we present the multilingual language model BLOOM-zh that features enhanced support for Traditional Chinese. BLOOM-zh has its origins in the open-source BLOOM models p…
Tree-Structured Semantic Encoder with Knowledge Sharing for Domain Adaptation in Natural Language Generation
Bo-Hsiang Tseng, Paweł Budzianowski, Yen-Chen Wu +1
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. Thi…
Addressing Objects and Their Relations: The Conversational Entity Dialogue Model
Stefan Ultes, Paweł Budzianowski, Iñigo Casanueva +5
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.,…
Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems
Bo-Hsiang Tseng, Florian Kreyssig, Pawel Budzianowski +4
Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling. Given a semantic representation provided by the dialogue manager, the lan…
Interactive Spoken Content Retrieval by Deep Reinforcement Learning
Yen-Chen Wu, Tzu-Hsiang Lin, Yang-De Chen +2
User-machine interaction is important for spoken content retrieval. For text content retrieval, the user can easily scan through and select on a list of retrieved item. This is imp…
A Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN) for Unsupervised Discovery of Linguistic Units and Generation of High Quality Features
Cheng-Tao Chung, Cheng-Yu Tsai, Hsiang-Hung Lu +5
This paper summarizes the work done by the authors for the Zero Resource Speech Challenge organized in the technical program of Interspeech 2015. The goal of the challenge is to di…