Sentence Correction using Recurrent Neural Networks

In this work, we propose that a pre-processing method for changing text data to conform closer to the distribution of standard English will help increase the performance of many state-of-the-art NLP models and algorithms when confronted with data taken “from the wild”. Our system receives as input a text word, sentence or paragraph which we assume contains (possibly none) random corruptions; formally, we say that the input comes from a corrupted language domain that is a superset of our target language domain. Our system then processes this input and outputs a “translation” or “projection” to our target language domain, with the goal of the output being to preserve the latent properties of the input text (sentiment, named entities, etc.) but mutated in a way that embeds these properties in a representation familiar to other NLP systems.