Making smart chat bots, that really understand what the user means, can be quite time consuming. A smart bot needs to be trained with an extensive set of expressions, and coming up with fifty or a hundred ways to express the same meaning can be hard, especially for people who are not used to it. In order to enhance the user experience for our clients that make use of our chat bot platform, we are currently implementing text generation. Based on some expressions provided by the user, we generate a number of similar expressions, using the most innovative text generation techniques. Additionally, our text generation system learns on the fly! The fine training capabilities allow us to tailor expression generation functionality in near-real time.
We confront approaches based on character- and word-level embeddings and explain their advantages and disadvantages. Finally, we discuss the importance of post-processing for filtering candidate expressions, using Part of Speech taggers and other NLP tools from very popular toolkits and libraries such as SpaCy, Gensim and NLTK.