Sentence Generation Using Selective Text Prediction

Samarth Navali, Jyothirmayi Kolachalam, Vanraj Vala


Text generation based on comprehensive datasets has been a well-known problem from several years. The biggest challenge is in creating a readable and coherent personalized text for specific user. Deep learning models have had huge success in the different text generation tasks such as script creation, translation, caption generation etc. Most of the existing methods require large amounts of data to perform simple sentence generation that may be used to greet the user or to give a unique reply. This research presents a novel and efficient method to generate sentences using a combination of Context Free Grammars and Hidden Markov Models. We have evaluated using two different methods, the first one is using a score similar to the BLEU score. The proposed implementation achieved 83% precision on the tweets dataset. The second method of evaluation being a subjective evaluation for the generated messages which is observed to be better than other methods.


Text generation, sentence generation, context free grammar, CFG, hidden Markov model, HMM, selective text prediction

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