no code implementations • 16 Dec 2023 • MingBin Xu, Alex Jin, Sicheng Wang, Mu Su, Tim Ng, Henry Mason, Shiyi Han, Zhihong Lei Yaqiao Deng, Zhen Huang, Mahesh Krishnamoorthy
With increasingly more powerful compute capabilities and resources in today's devices, traditionally compute-intensive automatic speech recognition (ASR) has been moving from the cloud to devices to better protect user privacy.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 16 Oct 2023 • Zhihong Lei, Ernest Pusateri, Shiyi Han, Leo Liu, MingBin Xu, Tim Ng, Ruchir Travadi, Youyuan Zhang, Mirko Hannemann, Man-Hung Siu, Zhen Huang
Recent advances in deep learning and automatic speech recognition have improved the accuracy of end-to-end speech recognition systems, but recognition of personal content such as contact names remains a challenge.
no code implementations • 10 Oct 2023 • Zhihong Lei, MingBin Xu, Shiyi Han, Leo Liu, Zhen Huang, Tim Ng, Yuanyuan Zhang, Ernest Pusateri, Mirko Hannemann, Yaqiao Deng, Man-Hung Siu
Recent advances in deep learning and automatic speech recognition (ASR) have enabled the end-to-end (E2E) ASR system and boosted the accuracy to a new level.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 18 Jul 2022 • MingBin Xu, Congzheng Song, Ye Tian, Neha Agrawal, Filip Granqvist, Rogier Van Dalen, Xiao Zhang, Arturo Argueta, Shiyi Han, Yaqiao Deng, Leo Liu, Anmol Walia, Alex Jin
Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP.
no code implementations • 5 Apr 2019 • Nargiza Nosirova, MingBin Xu, Hui Jiang
As a result, we observed competitive performance in nearly all of the tasks.
no code implementations • 5 Apr 2019 • Nargiza Nosirova, MingBin Xu, Hui Jiang
In this paper, we explore a new approach to named entity recognition (NER) with the goal of learning from context and fragment features more effectively, contributing to the improvement of overall recognition performance.
no code implementations • 29 Mar 2019 • Dekun Wu, Nana Nosirova, Hui Jiang, MingBin Xu
Question answering over knowledge base (KB-QA) has recently become a popular research topic in NLP.
no code implementations • 23 Feb 2019 • Xi Zhu, MingBin Xu, Hui Jiang
In this paper, we present our method of using fixed-size ordinally forgetting encoding (FOFE) to solve the word sense disambiguation (WSD) problem.
no code implementations • EMNLP 2018 • Sedtawut Watcharawittayakul, MingBin Xu, Hui Jiang
In this paper, we propose a new approach to employ the fixed-size ordinally-forgetting encoding (FOFE) (Zhang et al., 2015b) in neural languages modelling, called dual-FOFE.
no code implementations • EMNLP 2017 • Joseph Sanu, MingBin Xu, Hui Jiang, Quan Liu
In this paper, we propose to learn word embeddings based on the recent fixed-size ordinally forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence into a fixed-size representation.
no code implementations • ACL 2017 • Mingbin Xu, Hui Jiang, Sedtawut Watcharawittayakul
In this paper, we study a novel approach for named entity recognition (NER) and mention detection (MD) in natural language processing.
1 code implementation • 2 Nov 2016 • Mingbin Xu, Hui Jiang
In this paper, we study a novel approach for named entity recognition (NER) and mention detection in natural language processing.
1 code implementation • 6 May 2015 • Shiliang Zhang, Hui Jiang, MingBin Xu, JunFeng Hou, Li-Rong Dai
In this paper, we propose the new fixed-size ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation.