no code implementations • 21 Oct 2024 • Victor Junqiu Wei, Weicheng Wang, Di Jiang, Conghui Tan, Rongzhong Lian
Due to the rising awareness of privacy protection and the voluminous scale of speech data, it is becoming infeasible for Automatic Speech Recognition (ASR) system developers to train the acoustic model with complete data as before.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • ACL 2019 • Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, Xiyuan Zhang, Rongzhong Lian, Haifeng Wang
Konv enables a very challenging task as the model needs to both understand dialogue and plan over the given knowledge graph.
no code implementations • WS 2019 • Shaobo Cui, Rongzhong Lian, Di Jiang, Yuanfeng Song, Siqi Bao, Yong Jiang
DAL is the first work to innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses.
8 code implementations • 13 Jun 2019 • Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, Xiyuan Zhang, Rongzhong Lian, Haifeng Wang
DuConv enables a very challenging task as the model needs to both understand dialogue and plan over the given knowledge graph.
1 code implementation • ACL 2019 • Siqi Bao, Huang He, Fan Wang, Rongzhong Lian, Hua Wu
In this paper, a novel Generation-Evaluation framework is developed for multi-turn conversations with the objective of letting both participants know more about each other.
1 code implementation • 13 Feb 2019 • Rongzhong Lian, Min Xie, Fan Wang, Jinhua Peng, Hua Wu
Specifically, a posterior distribution over knowledge is inferred from both utterances and responses, and it ensures the appropriate selection of knowledge during the training process.
1 code implementation • 7 Feb 2019 • Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty, Jennifer Hicks, Sean F. Carroll, Bo Zhou, Hongsheng Zeng, Fan Wang, Rongzhong Lian, Hao Tian, Wojciech Jaśkowski, Garrett Andersen, Odd Rune Lykkebø, Nihat Engin Toklu, Pranav Shyam, Rupesh Kumar Srivastava, Sergey Kolesnikov, Oleksii Hrinchuk, Anton Pechenko, Mattias Ljungström, Zhen Wang, Xu Hu, Zehong Hu, Minghui Qiu, Jun Huang, Aleksei Shpilman, Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Lance Rane, Aditya Bhatt, Zhengfei Wang, Penghui Qi, Zeyang Yu, Peng Peng, Quan Yuan, Wenxin Li, Yunsheng Tian, Ruihan Yang, Pingchuan Ma, Shauharda Khadka, Somdeb Majumdar, Zach Dwiel, Yinyin Liu, Evren Tumer, Jeremy Watson, Marcel Salathé, Sergey Levine, Scott Delp
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
1 code implementation • 11 Aug 2018 • Di Jiang, Yuanfeng Song, Rongzhong Lian, Siqi Bao, Jinhua Peng, Huang He, Hua Wu
In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models.
1 code implementation • 31 Jul 2017 • Di Jiang, Zeyu Chen, Rongzhong Lian, Siqi Bao, Chen Li
Familia is an open-source toolkit for pragmatic topic modeling in industry.
no code implementations • COLING 2016 • Di Jiang, Lei Shi, Rongzhong Lian, Hua Wu
Topic modeling and word embedding are two important techniques for deriving latent semantics from data.