Fetching the paper…
Reading the bibliography…
Although n-gram language models (LMs) have been outperformed by the state-of-the-art neural LMs, they are still widely used in speech recognition due to its high efficiency in inference.
“A neural probabilistic language model,”
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin, · 2003
Earlier work this paper cites.
“Recurrent neural network based language model,”
Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur, · 2010
Earlier work this paper cites.
“A fast re-scoring strategy to capture long-distance dependencies,”
Anoop Deoras, Tomáš Mikolov, and Kenneth Church, · 2011
Earlier work this paper cites.
“Extensions of recurrent neural network language model,”
Tomáš Mikolov, Stefan Kombrink, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur, · 2011
Earlier work this paper cites.
“Generating text with recurrent neural networks,”
Ilya Sutskever, James Martens, and Geoffrey E Hinton, · 2011
Earlier work this paper cites.
“Lstm neural networks for language modeling,”
Martin Sundermeyer, Ralf Schlüter, and Hermann Ney, · 2012
Earlier work this paper cites.
“Converting neural network language models into back-off language models for efficient decoding in automatic speech recognition,”
Ebru Arısoy, Stanley F Chen, Bhuvana Ramabhadran, and Abhinav Sethy, · 2013
Earlier work this paper cites.
“Converting continuous-space language models into n-gram language models for statistical machine translation,”
Rui Wang, Masao Utiyama, Isao Goto, Eiichro Sumita, Hai Zhao, and Bao-Liang Lu, · 2013
Cited alongside, same era.
“Comparing approaches to convert recurrent neural networks into backoff language models for efficient decoding,”
Heike Adel, Katrin Kirchhoff, Ngoc Thang Vu, Dominic Telaar, and Tanja Schultz, · 2014
Cited alongside, same era.
“Combinations of various language model technologies including data expansion and adaptation in spontaneous speech recognition,”
Ryo Masumura, Taichi Asami, Takanobu Oba, Hirokazu Masataki, Sumitaka Sakauchi, and Akinori Ito, · 2015
Cited alongside, same era.
“Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,”
William Chan, Navdeep Jaitly, Quoc Le, and Oriol Vinyals, · 2016
Cited alongside, same era.
“Neural machine translation of rare words with subword units,”
Rico Sennrich, Barry Haddow, and Alexandra Birch, · 2016
“The microsoft 2017 conversational speech recognition system,”
Wayne Xiong, Lingfeng Wu, Fil Alleva, Jasha Droppo, Xuedong Huang, and Andreas Stolcke, · 2018
Later among the works it cites.
“Improving language understanding by generative pre-training,”
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever, · 2018
Later among the works it cites.
“Improvements to n-gram language model using text generated from neural language model,”
Masayuki Suzuki, Nobuyasu Itoh, Tohru Nagano, Gakuto Kurata, and Samuel Thomas, · 2019
Closest in time.
“Bert: Pre-training of deep bidirectional transformers for language understanding,”
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, · 2019
Closest in time.
“Language models are unsupervised multitask learners,”
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever, · 2019
Closest in time.
“From senones to chenones: Tied context-dependent graphemes for hybrid speech recognition,”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
“Attention is all you need,”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin, · 2017
Cited alongside, same era.
Duc Le, Xiaohui Zhang, Weiyi Zheng, Christian Fügen, Geoffrey Zweig, and Michael Seltzer, · 2019
Closest in time.