Keisuke Sakaguchi Title: "Robust Text Correction for Grammar and Fluency" Abstract: Robustness has always been a desirable property for natural language processing. In many cases, NLP models (e.g., parsing) and downstream applications (e.g., MT) perform poorly when the input contains noise such as spelling errors, grammatical errors, and disfluency. In this talk, I will present three recent results on error correction models: character, word, and sentence level respectively. For character level, I propose semi-character recurrent neural network, which is motivated by a finding in Psycholinguistics, called Cmabrigde Uinervtisy (Cambridge University) effect. For word-level robustness, I propose an error-repair dependency parsing algorithm for ungrammatical texts.