Negative language transfer in learner E nglish: A new dataset (2021)

Abstract: Automatic personalized corrective feedback can help language learners from different backgrounds better acquire a new language. This paper introduces a learner English dataset in which learner errors are accompanied by information about possible error sources. This dataset contains manually annotated error causes for learner writing errors. These causes tie learner mistakes to structures from their first languages, when the rules in English and in the first language diverge. This new dataset will enable second language acquisition researchers to computationally analyze a large quantity of learner errors that are related to language transfer from the learners’ first language. The dataset can also be applied in personalizing grammatical error correction systems according to the learners’ first language and in providing feedback that is informed by the cause of an error.

## introduction english has become an international language. it is the lingua-franca that unites native speakers of other languages around the world @xcite . for that reason, it is not hard to believe that the teaching of english as a second language foot_0 has caught a lot of attention from the research community @xcite . over the years, computational linguistics researchers have collected corpora containing text written by language learners. these corpora have made possible several advances in language teaching, such as automatic writing assessment @xcite and automatic error detection and correction @xcite @xcite . although learner corpora are used to model grammatical error correction systems, they are not as often employed in the enhancement of learner feedback. language learners benefit from direct corrective feedback @xcite . moreover, feedback that makes them reflect upon their errors and distinguish a cause for their mistakes correlates to increased performance @xcite @xcite . in this paper, we introduce a learner english dataset enhanced with error cause information and concrete examples of learner errors that relate to the learners' first language. it has the potential to help create computational models that provide personalized feedback to english language learners based on the learners' native languages. this new dataset can be accessed by following the instructions described in our research group's repository foot_1 . the dataset presented in this paper contains supplementary explanations for errors made by chinese native speakers when writing in english. chinese learners represent a growing share of the english as a second language market. a nationwide language survey from the chinese government reports that at the beginning of 2001 at least one third of china's population was learning a new language and out of those, 93% were learning english @xcite . these numbers have only seemed to increase in recent years. the latest survey of international students in the us that was conducted by the institute of international education (2020) shows that 35% of these students come from china. with that in mind, it is reasonable to say that this large portion of english learners can benefit from receiving personalized feedback on their writing errors. ## annotation scheme the annotation scheme was designed to highlight the relationship between the learner error and the learner's l1. other than the boolean label representing whether an error is related to negative language transfer, each entry carries information about the possible reason behind that learner mistake. even when the relationship between the error and the learner's l1 is not apparent, the annotation scheme will provide a possible cause for the error. this cause is not related to language transfer. the error cause feature was heavily influenced by language teacher guides, books that aim to make teachers aware of the learner errors they can encounter in the classroom, e.g., "learner english: a teacher's guide to interference and other problems" by @xcite . guides like these have been written based on years of in-classroom experience and contain information about error causes along with potential learner feedback. these guides were used as a baseline for negative language transfer detection during the annotation process. other important sources of guidance for the error cause feature annotation were chinese and english grammar books and guides foot_2 @xcite . these sources allowed the direct contrast of erroneous utterances with language rules and this contrast enabled the derivation of possible causes for learner mistakes. ## dataset application second language acquisition researchers and language teachers are well acquainted with learner errors that are related to the learners' l1s. these communities have produced comprehensive guides to learner errors and their causes. learner language guides allow learners and teachers to identify the reasons behind certain error types and, with that, better understand and prevent those mistakes. in some of these guides the reader will find information that connects learners' l1s with common error types committed by native speakers of that language. our new dataset, enables the use of other indicators, such as linguistic features and proficiency levels, to identify errors related to negative language transfer. to understand the effect of linguistic features in negative language transfer prediction, we built classification models to predict when a learner error is related to negative language transfer. we wanted to explore the relationship between negative language transfer and the linguistic features of errors, such as part-of-speech (pos) tags and dependency labels, since these features are made available by this new dataset. ## conclusion our dataset is the first we are aware of that annotates a large amount of learner english data with negative language transfer features and error causes. it has the potential to improve the performance of computational linguistics tasks, such as native language transfer identification and grammatical error correction. more importantly, its content can benefit english teachers and learners by making more personalized error feedback available. another potential application of the dataset is in the automatic detection of negative language transfer. this application could help provide real-time l1-informed feedback to english learners. our research group is currently working on annotating errors from other l1 learner groups. we are also expanding our annotation process to learner data from sources other than the fce dataset, such as the lang-8 english corpus @xcite . with that, we hope to broaden the scope of english learners supported by l1 informed error feedback.