This work examines the impact of cross-linguistic transfer on grammatical errors in English as Second Language (ESL) texts. Using a computational framework that formalizes the theory of Contrastive Analysis (CA), we demonstrate that language specific error distributions in ESL writing can be predicted from the typological properties of the native language and their relation to the typology of English. Our typology driven model enables to obtain accurate estimates of such distributions without access to any ESL data for the target languages. Furthermore, we present a strategy for adjusting our method to low-resource languages that lack typological documentation using a bootstrapping approach which approximates native language typology from ESL texts. Finally, we show that our framework is instrumental for linguistic inquiry seeking to identify first language factors that contribute to a wide range of difficulties in second language acquisition.
Previous work on grammatical error correction that examined determiner and preposition errors @cite_3 @cite_8 incorporated native language specific priors in models that are otherwise trained on standard English text. Our work extends the native language tailored treatment of grammatical errors to a much larger set of error types. More importantly, this approach is limited by the availability of manual error annotations for the target language in order to obtain the required error counts. Our framework enables to bypass this annotation bottleneck by predicting language specific priors from typological information.
@cite_3 @cite_8
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In this paper, we consider the problem of generating candidate corrections for the task of correcting errors in text. We focus on the task of correcting errors in preposition usage made by non-native English speakers, using discriminative classifiers. The standard approach to the problem assumes that the set of candidate corrections for a preposition consists of all preposition choices participating in the task. We determine likely preposition confusions using an annotated corpus of non-native text and use this knowledge to produce smaller sets of candidates. We propose several methods of restricting candidate sets. These methods exclude candidate prepositions that are not observed as valid corrections in the annotated corpus and take into account the likelihood of each preposition confusion in the non-native text. We find that restricting candidates to those that are observed in the non-native data improves both the precision and the recall compared to the approach that views all prepositions as possible candidates. Furthermore, the approach that takes into account the likelihood of each preposition confusion is shown to be the most effective. We show that existing methods for training preposition error correction systems, whether using well-edited text or error-annotated corpora, do not generalize across very different test sets. We present a new, large errorannotated corpus and use it to train systems that generalize across three different test sets, each from a different domain and with different error characteristics. This new corpus is automatically extracted from Wikipedia revisions and contains over one million instances of preposition corrections. English as a Second Language (ESL) learners’ writings contain various grammatical errors. Previous research on automatic error correction for ESL learners’ grammatical errors deals with restricted types of learners’ errors. Some types of errors can be corrected by rules using heuristics, while others are difficult to correct without statistical models using native corpora and or learner corpora. Since adding error annotation to learners’ text is time-consuming, it was not until recently that large scale learner corpora became publicly available. However, little is known about the effect of learner corpus size in ESL grammatical error correction. Thus, in this paper, we investigate the effect of learner corpus size on various types of grammatical errors, using an error correction system based on phrase-based statistical machine translation (SMT) trained on a large scale errortagged learner corpus. We show that the phrase-based SMT approach is effective in correcting frequent errors that can be identified by local context, and that it is difficult for phrase-based SMT to correct errors that need long range contextual information. Incorrect usage of prepositions and determiners constitute the most common types of errors made by non-native speakers of English. It is not surprising, then, that there has been a significant amount of work directed towards the automated detection and correction of such errors. However, to date, the use of different data sets and different task definitions has made it difficult to compare work on the topic. This paper reports on the HOO 2012 shared task on error detection and correction in the use of prepositions and determiners, where systems developed by 14 teams from around the world were evaluated on the same previously unseen errorful text.