Sequence-to-sequence Bangla sentence generation with LSTM recurrent neural networks

MS Islam, SSS Mousumi, S Abujar… - Procedia Computer …, 2019 - Elsevier
MS Islam, SSS Mousumi, S Abujar, SA Hossain
Procedia Computer Science, 2019Elsevier
Sequence to sequence text generation is the most efficient approach for automatically
converting the script of a word from a source sequence to a target sequence. Text
generation is the application of natural language generation which is useful in sequence
modeling like the machine translation, speech recognition, image captioning, language
identification, video captioning and much more. In this paper we have discussed about
Bangla text generation, using deep learning approach, Long Short-term Memory (LSTM), a …
Abstract
Sequence to sequence text generation is the most efficient approach for automatically converting the script of a word from a source sequence to a target sequence. Text generation is the application of natural language generation which is useful in sequence modeling like the machine translation, speech recognition, image captioning, language identification, video captioning and much more. In this paper we have discussed about Bangla text generation, using deep learning approach, Long Short-term Memory (LSTM), a special kind of RNN (Recurrent Neural Network). LSTM networks are suitable for analyzing sequences of text data and predicting the next word. LSTM could be a respectable solution if you want to predict the very next point of a given time sequence. In this article we proposed a artificial Bangla Text Generator with LSTM, which is very early for this language and also this model is validated with satisfactory accuracy rate.
Elsevier