Exploring the interplay of mode of discourse and proficiency level in ESL writing performance

Recent theory in discourse and practice in rhetoric has suggested that writers require different skills and strategies when writing for different purposes, and in using different genres and modes (Kinneavy, 1972; Carrell and Connor, 1991) in writing. The importance of taking into account these various aspectual skills and forms of writing is recognised in teaching (e.g. Scarcella and Oxford, 1992), and in the assessment of writing (e.g. Odell and Cooper, 1980). For instance, Odell and Cooper argued that any claims about writing ability cannot be made until students’ performance on a variety of writing tasks has been examined. Thus, the issue of what writing task(s) are to be included in a test is crucial, since a task will be regarded as useless if it does not provide the basis for making generalisations regarding an individual’s writing ability. This paper presents the findings of a study on the effects of mode of discourse on L2 writing performance as well as the interplay between learner variable, namely, proficiency level and task variable, mode of discourse amongst Malaysian upper secondary ESL learners. The findings provide some evidence for the need to re-examine issues of reliability and validity in test practice of manipulating variables in the design of assessment tasks to evaluate ESL writing performance. Given the status and complexity of the writing skill, it stands to reason that studies into this area will continue to shed light onto how best the construct can be understood, taught and tested to give a fair chance for language learners to exhibit their true ability and be reliably reported on

Examining Scientific Writing Styles from the Perspective of Linguistic Complexity

Publishing articles in high-impact English journals is difficult for scholars around the world, especially for non-native English-speaking scholars (NNESs), most of whom struggle with proficiency in English. In order to uncover the differences in English scientific writing between native English-speaking scholars (NESs) and NNESs, we collected a large-scale data set containing more than 150,000 full-text articles published in PLoS between 2006 and 2015. We divided these articles into three groups according to the ethnic backgrounds of the first and corresponding authors, obtained by Ethnea, and examined the scientific writing styles in English from a two-fold perspective of linguistic complexity: (1) syntactic complexity, including measurements of sentence length and sentence complexity; and (2) lexical complexity, including measurements of lexical diversity, lexical density, and lexical sophistication. The observations suggest marginal differences between groups in syntactical and lexical complexity.Comment: 6 figure

Automatic assessment of spoken language proficiency of non-native children

This paper describes technology developed to automatically grade Italian students (ages 9-16) on their English and German spoken language proficiency. The students' spoken answers are first transcribed by an automatic speech recognition (ASR) system and then scored using a feedforward neural network (NN) that processes features extracted from the automatic transcriptions. In-domain acoustic models, employing deep neural networks (DNNs), are derived by adapting the parameters of an original out of domain DNN

Experiments with Universal CEFR Classification

The Common European Framework of Reference (CEFR) guidelines describe language proficiency of learners on a scale of 6 levels. While the description of CEFR guidelines is generic across languages, the development of automated proficiency classification systems for different languages follow different approaches. In this paper, we explore universal CEFR classification using domain-specific and domain-agnostic, theory-guided as well as data-driven features. We report the results of our preliminary experiments in monolingual, cross-lingual, and multilingual classification with three languages: German, Czech, and Italian. Our results show that both monolingual and multilingual models achieve similar performance, and cross-lingual classification yields lower, but comparable results to monolingual classification.Comment: to appear in the proceedings of The 13th Workshop on Innovative Use of NLP for Building Educational Application

Self-imitating Feedback Generation Using GAN for Computer-Assisted Pronunciation Training

Self-imitating feedback is an effective and learner-friendly method for non-native learners in Computer-Assisted Pronunciation Training. Acoustic characteristics in native utterances are extracted and transplanted onto learner's own speech input, and given back to the learner as a corrective feedback. Previous works focused on speech conversion using prosodic transplantation techniques based on PSOLA algorithm. Motivated by the visual differences found in spectrograms of native and non-native speeches, we investigated applying GAN to generate self-imitating feedback by utilizing generator's ability through adversarial training. Because this mapping is highly under-constrained, we also adopt cycle consistency loss to encourage the output to preserve the global structure, which is shared by native and non-native utterances. Trained on 97,200 spectrogram images of short utterances produced by native and non-native speakers of Korean, the generator is able to successfully transform the non-native spectrogram input to a spectrogram with properties of self-imitating feedback. Furthermore, the transformed spectrogram shows segmental corrections that cannot be obtained by prosodic transplantation. Perceptual test comparing the self-imitating and correcting abilities of our method with the baseline PSOLA method shows that the generative approach with cycle consistency loss is promising