1 code implementation • 29 Nov 2022 • Guangsen Wang, Shafiq Joty, Junnan Li, Steven Hoi
BotSIM adopts a layered design comprising the infrastructure layer, the adaptor layer and the application layer.
1 code implementation • 22 Nov 2022 • Guangsen Wang, Samson Tan, Shafiq Joty, Gang Wu, Jimmy Au, Steven Hoi
We have open-sourced the toolkit at https://github. com/salesforce/botsim
1 code implementation • 15 Sep 2022 • Dongxu Li, Junnan Li, Hung Le, Guangsen Wang, Silvio Savarese, Steven C. H. Hoi
We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications.
no code implementations • 3 Dec 2020 • Genta Indra Winata, Guangsen Wang, Caiming Xiong, Steven Hoi
One crucial challenge of real-world multilingual speech recognition is the long-tailed distribution problem, where some resource-rich languages like English have abundant training data, but a long tail of low-resource languages have varying amounts of limited training data.
no code implementations • 8 Apr 2020 • Weiran Wang, Guangsen Wang, Aadyot Bhatnagar, Yingbo Zhou, Caiming Xiong, Richard Socher
For Switchboard, our phone-based BPE system achieves 6. 8\%/14. 4\% word error rate (WER) on the Switchboard/CallHome portion of the test set while joint decoding achieves 6. 3\%/13. 3\% WER.
no code implementations • 23 Oct 2019 • Xingchen Song, Guangsen Wang, Zhiyong Wu, Yiheng Huang, Dan Su, Dong Yu, Helen Meng
Our best systems achieve a relative improvement of 11. 9% and 8. 3% on the TIMIT and WSJ tasks respectively.
no code implementations • 19 Sep 2019 • Yiheng Huang, Jinchuan Tian, Lei Han, Guangsen Wang, Xingcheng Song, Dan Su, Dong Yu
One important challenge of training an NNLM is to leverage between scaling the learning process and handling big data.
no code implementations • 2 Sep 2019 • Yiheng Huang, Liqiang He, Lei Han, Guangsen Wang, Dan Su
In this work, we propose to train pruned language models for the word classes to replace the slots in the root n-gram.
no code implementations • 5 Feb 2016 • Kong Aik Lee, Ville Hautamäki, Anthony Larcher, Wei Rao, Hanwu Sun, Trung Hieu Nguyen, Guangsen Wang, Aleksandr Sizov, Ivan Kukanov, Amir Poorjam, Trung Ngo Trong, Xiong Xiao, Cheng-Lin Xu, Hai-Hua Xu, Bin Ma, Haizhou Li, Sylvain Meignier
This article describes the systems jointly submitted by Institute for Infocomm (I$^2$R), the Laboratoire d'Informatique de l'Universit\'e du Maine (LIUM), Nanyang Technology University (NTU) and the University of Eastern Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE).