Group sparse CNNs for question classification with answer sets
Mingbo Ma, Liang Huang, et al.
ACL 2017
Speech-to-speech translation has evolved into an attractive area in recent years with significant progress made by various research groups. However, the translation engines usually suffer from the lack of bilingual training data, especially for low-resource languages. In this paper we present an unsupervised training technique to alleviate this problem by taking advantage of available source language data. Different approaches are proposed and compared through extensive experiments conducted on a speech-to-speech translation task between Farsi and English. The translation performance is significantly improved in both directions with the enhanced translation model. A state-of-the-art Farsi automatic speech recognition system is also established in this work. ©2008 IEEE.
Mingbo Ma, Liang Huang, et al.
ACL 2017
Bowen Zhou, Bing Xiang, et al.
SSST 2008
Mo Yu, Wenpeng Yin, et al.
ACL 2017
Jia Cui, Yonggang Deng, et al.
ASRU 2009