Teaching remedial grammar through data-driven learning using AntPConc

In most Asian countries, students receive between six and eight years of compulsory English education before they enter university. Despite this massive investment in English education, many students, especially in Japan, continue to show a poor understanding of rudimentary grammar rules. In this paper we report on a unique English course designed specifically to address grammar issues at low (remedial) levels using a Data-Driven Learning (DDL) approach. Applications of DDL are becoming more widely reported, but they are generally at the intermediate or advanced level. One of the challenges of using DDL at the remedial level is the lack of suitably leveled corpora. Another challenge is that most corpus tools used in DDL are designed for researchers or advanced learners and thus can appear overly complex. To address these issues, we have developed a simple English corpus built from standard school texts. We have also created a freeware, parallel corpus tool, AntPConc, that is specially designed to be simple, easy, and intuitive to use by beginner learners. Results from the course show significant gains between pre- and post-tests of grammar understanding for beginner-level EFL university students. We also obtained positive student feedback on the AntPConc software

A survey of studies in systemic functional language description and typology

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A systemic approach to translating style: a comparative study of four Chinese translations of Hemingway’s The Old Man and the Sea

The visibility of translators in translated texts has been increasingly recognised, yet research on the translator’s voice and the methodological issues concerned has remained sparse. Corpus-based methods allow only limited access to the motivation of the translator’s choices, and need to be complemented by other research tools to form a coherent methodology for investigating a translator’s style. The thesis adopts an interdisciplinary approach, combining systemic linguistics and corpus studies with sociohistorical research within a descriptive framework to study the translator’s discursive presence in the text. This approach is as yet underexplored in translation studies. My work examines four Chinese translations of Hemingway’s The Old Man and the Sea (1952), by Hai Guan (1956), Wu Lao (1987), Li Xiyin (1987) and Zhao Shaowei (1987). The investigation concerns the rendering of transitivity, modality, direct speech and free direct thought presentation as well as the transitions of modes of point of view. It also inquires into the causes of the variation in style between the four translators. I map textual features onto specific sociocultural and ideological contexts of production in an attempt to identify correlations between them. Another objective is to test the applicability of Halliday’s transitivity model (1994) and Simpson’s model of point of view (1993) to the analysis of Chinese translated texts, and to explore possible adjustments to these models to make them serviceable for translation comparison between English and Chinese. The thesis has six chapters: (1) Theoretical approaches, methodological tools and framework, (2) Location of the texts within the sociocultural contexts, (3) Translation of the transitivity system, (4) Translation of point of view, (5) Critical analysis of individual examples and (6) Motivations for translation shifts

Authorship identification of translation algorithms.

Authorship analysis is a process of identifying a true writer of a given document and has been studied for decades. However, only a handful of studies of authorship analysis of translators are available despite the fact that online translations are widely available and also popularly employed in automatic translations of posts in social networking services. The identification of translation algorithms has potential to contribute to the investigation of cybercrimes, involving translation of scam messages by algorithmic translations to reach speakers of foreign languages. This study tested bag of words (BOW) approach in authorship attribution and the existing approaches to translator attribution. We also proposed a simple but accurate feature that extracts the combinations of lexical and syntactic information from texts. Our experiments show that the proposed feature is text size invariant