The AFRL IWSLT 2020 Systems: Work-From-Home Edition
暂无分享,去创建一个
[1] David R. Heam. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the United States Air Force. , 2017 .
[2] Alexander M. Rush,et al. OpenNMT: Neural Machine Translation Toolkit , 2018, AMTA.
[3] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[4] Yiming Wang,et al. A Pruned Rnnlm Lattice-Rescoring Algorithm for Automatic Speech Recognition , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[5] Katherine Young,et al. The AFRL WMT18 Systems: Ensembling, Continuation and Combination , 2018, WMT.
[6] Jeremy Gwinnup,et al. The AFRL WMT19 Systems: Old Favorites and New Tricks , 2019, WMT.
[7] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[8] Yannick Estève,et al. TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation , 2018, SPECOM.
[9] Tomas Mikolov,et al. Bag of Tricks for Efficient Text Classification , 2016, EACL.
[10] Taku Kudo,et al. SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing , 2018, EMNLP.
[11] André F. T. Martins,et al. Marian: Fast Neural Machine Translation in C++ , 2018, ACL.
[12] Daniel Povey,et al. The Kaldi Speech Recognition Toolkit , 2011 .
[13] Eric G. Hansen,et al. The MITLL-AFRL IWSLT 2016 Systems , 2016, IWSLT.