Abstract
During last decade, language modeling has been dominated by neural structures; RNN, LSTM or Transformer. These neural language models provide excellent performance to the detriment of very high computational cost. This work investigates the use of probabilistic language model that requires much less computational cost. In particular, we are interested in variable-order Markov model that can be efficiently implemented on a probabilistic suffix tree (PST) structure. The PST construction is cheap and can be easily scaled to very large dataset. Experimental results show that this model can be used to generated realistic sentences.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
