1 citations · 1 across the 2 of their papers we have counts for
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GSQA: An End-to-End Model for Generative Spoken Question Answering
Min-Han Shih, Ho-Lam Chung, Yu-Chi Pai +4
In recent advancements in spoken question answering (QA), end-to-end models have made significant strides. However, previous research has primarily focused on extractive span selec…
Bridging Speech and Textual Pre-trained Models with Unsupervised ASR
Jiatong Shi, Chan-Jan Hsu, Holam Chung +5
Spoken language understanding (SLU) is a task aiming to extract high-level semantics from spoken utterances. Previous works have investigated the use of speech self-supervised mode…
T5lephone: Bridging Speech and Text Self-supervised Models for Spoken Language Understanding via Phoneme level T5
Chan-Jan Hsu, Ho-Lam Chung, Hung-yi Lee +1
In Spoken language understanding (SLU), a natural solution is concatenating pre-trained speech models (e.g. HuBERT) and pretrained language models (PLM, e.g. T5). Most previous wor…
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies
Ho-Lam Chung, Ying-Hong Chan, Yao-Chung Fan
In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods. First, the quality of the existing DG methods are still far from pr…