no code implementations • 23 Aug 2023 • Hengyuan Zhang, Peng Chang, Zongcheng Ji
This research marks the first application of large language models to table-based question answering tasks, enhancing the model's comprehension of both table structures and content.
no code implementations • 15 Apr 2023 • Ruchao Fan, Wei Chu, Peng Chang, Abeer Alwan
During inference, an error-based alignment sampling method is investigated in depth to reduce the alignment mismatch in the training and testing processes.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
1 code implementation • 6 May 2022 • Yuan Gong, Ziyi Chen, Iek-Heng Chu, Peng Chang, James Glass
Automatic pronunciation assessment is an important technology to help self-directed language learners.
Ranked #2 on Phone-level pronunciation scoring on speechocean762 (using extra training data)
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • CVPR 2021 • Yuxing Tang, Zhenjie Cao, Yanbo Zhang, Zhicheng Yang, Zongcheng Ji, Yiwei Wang, Mei Han, Jie Ma, Jing Xiao, Peng Chang
Starting with a fully supervised model trained on the data with pixel-level masks, the proposed framework iteratively refines the model itself using the entire weakly labeled data (image-level soft label) in a self-training fashion.
no code implementations • 18 Jun 2021 • Ruchao Fan, Wei Chu, Peng Chang, Jing Xiao, Abeer Alwan
For the analyses, we plot attention weight distributions in the decoders to visualize the relationships between token-level acoustic embeddings.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 28 Oct 2020 • Ruchao Fan, Wei Chu, Peng Chang, Jing Xiao
The information are used to extract acoustic representation for each token in parallel, referred to as token-level acoustic embedding which substitutes the word embedding in autoregressive transformer (AT) to achieve parallel generation in decoder.
no code implementations • 6 Feb 2020 • Peng Chang, Taskin Padir
This paper addresses a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim) to bridge the gap between simulation and real-world, and automate a flexible object manipulation task.
Robotics