Non-native text analysis: A survey

Abstract

Non-native speakers of English far outnumber native speakers; English is the main language of books, newspapers, airports, air-traffic control, international business, academic conferences, science, technology, diplomacy, sports, international competitions, pop music, and advertising (British Council 2014). Online education in the form of massive online open courses is also primarily in English—even teaching English. This creates enormous amounts of text written by non-native speakers, which in turn generates a need for grammar correction and analysis. Even aside from massive online open courses, the number of English learners in Asia alone is in the tens of millions. In this paper, we provide a survey of the two main areas of existing work on non-native text analysis, prefaced by an overview of common datasets used by researchers, comparing their attributes and potential uses. Then, an introduction to native language identification follows: determining the native language of an author based on text in the second language. This section is subdivided into various techniques and a shared task on this classification problem. Next, we discuss non-native grammatical error correction—finding and modifying text to fix errors or to make it sound more fluent. Again, we discuss different methods before investigating a relevant shared task. Lastly, we end with conclusions and potential future directions. While this survey primarily focuses on detecting and correcting non-nativeEnglishtext, many approaches are general and can be used across any language pairing.

Word Etymology as Native Language Interference

“…The study of native language interference with the learning of English can be used in multiple ways, including devising methods to make the learning easier and correcting language errors (Leacock et al, 2014;Gamon, 2010;Dahlmeier and Ng, 2011). Massung and Zhai (2016) present an overview of approaches to the task of natural language identification (NLI). Various surface indicators hold clues about a speaker's native language, that make their way into language production in a non-native language.…”

Evidence and Interpretation in Language Learning Research: Opportunities for Collaboration With Computational Linguistics

“…At the same time, the quantitatively best approaches (cf. Massung & Zhai, ) are based on thousands of shallow features (such as word and part‐of‐speech n‐grams), which do not lend themselves to a qualitative interpretation. Fostering more interdisciplinary discussion in this area stands to make the results of the CL research more relevant to SLA research on L1 transfer.…”

Combining Textual and Speech Features in the NLI Task Using State-of-the-Art Machine Learning Techniques

“…An exhaustive overview of NLI until 2014 has been provided by Massung and Zhai (2016). In Table 1 we show the basic characteristics of the datasets widely used so far.…”