Grammar Error Correction in Morphologically Rich Languages: The Case of Russian

Grammar Error Correction in Morphologically Rich Languages: The Case of Russian Alla Rozovskaya Queens College, City University of New York arozovskaya@qc.cuny.edu Dan Roth University of Pennsylvania danroth@seas.upenn.edu Abstract Until now, most of the research in grammar error correction focused on English, and the problem has hardly been explored for other languages. We address the task of correcting writing mistakes in morphologically rich lan- guages, with a focus on Russian. We present a corrected and error-tagged corpus of Russian learner writing and develop models that make use of existing state-of-the-art methods that have been well studied for English. Although impressive results have recently been achieved for grammar error correction of non-native English writing, these results are limited to domains where plentiful training data are avail- able. Because annotation is extremely costly, these approaches are not suitable for the majority of domains and languages. We thus focus on methods that use ‘‘minimal super- vision’’; that is, those that do not rely on large amounts of annotated training data, and show how existing minimal-supervision approaches extend to a highly inflectional language such as Russian. The results demonstrate that these methods are particularly useful for correcting mistakes in grammatical phenomena that involve rich morphology. 1 Introduction This paper addresses the task of correcting errors in text. Most of the research in the area of gram- mar error correction (GEC) focused on correcting mistakes made by English language learners. One standard approach to dealing with these errors, which proved highly successful in text correction competitions (Dale and Kilgarriff, 2011; Dale et al., 2012; Ng et al., 2013, 2014; Rozovskaya et al., 2017), makes use of a machine- learning classifier paradigm and is based on the methodology for correcting context-sensitive spelling mistakes (Golding and Roth, 1996, 1999; Banko and Brill, 2001). In this approach, classifiers are trained for a particular mistake type: for example, preposition, article, or noun number (Tetreault et al., 2010; Gamon, 2010; Rozovskaya and Roth, 2010c,b; Dahlmeier and Ng, 2012). Originally, classifiers were trained on native English data. As several annotated learner datasets became available, models were also trained on annotated learner data. More recently, the statistical machine trans- lation (MT) methods, including neural MT, have gained considerable popularity thanks to the availability of large annotated corpora of learner writing (e.g., Yuan and Briscoe, 2016; Junczys- Dowmunt and Grundkiewicz, 2016; Chollampatt and Ng, 2018). Classification methods work very well on well-defined types of errors, whereas MT is good at correcting interacting and complex types of mistakes, which makes these approaches complementary in some respects (Rozovskaya and Roth, 2016). Thanks to the availability of large (in-domain) datasets, substantial gains in performance have been made in English grammar correction. Un- fortunately, research on other languages has been scarce. Previous work includes efforts to create annotated learner corpora for Arabic (Zaghouani et al., 2014), Japanese (Mizumoto et al., 2011), and Chinese (Yu et al., 2014), and shared tasks on Arabic (Mohit et al., 2014; Rozovskaya et al., 2015) and Chinese error detection (Lee et al., 2016; …

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