The Development of Collocations as Constructions in L2 Writing

Cross-sectional and longitudinal learner corpus studies utilizing phraseological, frequency, and association strength approaches to phraseological unit identification have shown how the use of phraseological units varies across proficiency levels and develops over time. However, these methods suffer from several limitations, such as a reliance on native speaker intuition, a limited focus on contiguous word sequences, and a neglect of part of speech information in association strength calculation. This study seeks to address these limitations by defining lexical collocations as constructions (henceforth “collconstructions”) within the framework of Construction Grammar and investigating their cross-sectional variation and longitudinal development in two corpora of L2 writing. The cross-sectional corpus consisted of beginner and intermediate EFL learner texts assessed for overall writing proficiency, while the longitudinal corpus contained freewrites produced by ESL learners over the course of one year. Contiguous and non-contiguous adjective-noun, verb-noun, and adverb-adjective collconstruction tokens were extracted from each learner text in the two learner corpora. Each learner text was assessed for multiple constructional and collostructional indices of collconstruction production. Constructional indices included type frequencies, token frequencies, and normalized entropy scores for each collconstruction category. Collostructional indices consisted of proportion scores for different categories of adjective-noun, adverb-adjective, and verb-noun collconstruction types and tokens based on covarying collexeme scores calculated using frequency information from an academic reference corpus. Variation across proficiency levels was evaluated both qualitatively and quantitatively. The qualitative analysis consisted of examining variation in the production of specific functional collconstruction subcategories from a Usage-based Second Language Acquisition perspective. The quantitative analysis consisted of the calculation of an ordinal logistic regression in order to determine whether any indices of collconstruction production were predictive of L2 writing quality. Longitudinal development at the group level was investigated through the use of linear mixed effects models. Development for individual learners was examined from a Dynamic Systems Theory perspective that focuses on the role of variability in language development as well as interconnected development for multiple indices of collconstruction production. This study has important implications for future research on L2 phraseology research and second language acquisition research as well as phraseology pedagogy

The relationship between listening and other language skills in international English language testing system

Listening comprehension is the primary channel of learning a language. Yet of the four dominant macro-skills (listening, speaking, reading and writing), it is often difficult and inaccessible for second and foreign language learners due to its implicit process. The secondary skill, speaking, proceeds listening cognitively. Aural/oral skills precede the graphic skills, such as reading and writing, as they form the circle of language learning process. However, despite the significant relationship with other language skills, listening comprehension is treated lightly in the applied linguistics research. Half of our daily conversation and three quarters of classroom interaction are virtually devoted to listening comprehension. To examine the relationship of listening skill with other language skills, the outcome of 1800 Iranian participants undertaking International English Language Testing System (IELTS) in Tehran indicates the close correlation between listening comprehension and the overall language proficiency

Distributional effects and individual differences in L2 morphology learning

Second language (L2) learning outcomes may depend on the structure of the input and learners’ cognitive abilities. This study tested whether less predictable input might facilitate learning and generalization of L2 morphology while evaluating contributions of statistical learning ability, nonverbal intelligence, phonological short-term memory, and verbal working memory. Over three sessions, 54 adults were exposed to a Russian case-marking paradigm with a balanced or skewed item distribution in the input. Whereas statistical learning ability and nonverbal intelligence predicted learning of trained items, only nonverbal intelligence also predicted generalization of case-marking inflections to new vocabulary. Neither measure of temporary storage capacity predicted learning. Balanced, less predictable input was associated with higher accuracy in generalization but only in the initial test session. These results suggest that individual differences in pattern extraction play a more sustained role in L2 acquisition than instructional manipulations that vary the predictability of lexical items in the input

CLOZE TESTING : Analysis and Problems

The cloze procedure, originated by Wilson L. Taylor (1953), has received considerable attention in the field of testing English as a second or foreign language. Donald K. Darnell (1968) and John W. Oller (1973a), as well as many other researchers, have acknowledged its importance. Previous studies have provided rather convincing support for the value of cloze tests. Some specialists, however, have begun questioning the principle and methods of the cloze test as a formal instrument of measuring the proficiency of English as a second or foreign language. The writer attempts to present in this paper an overview of the theory underlying the cloze test, major findings concerning the techniques of conducting the test, and finally to point out certain problems of the test

Deep Learning for Automatic Assessment and Feedback of Spoken English

Growing global demand for learning a second language (L2), particularly English, has led to considerable interest in automatic spoken language assessment, whether for use in computerassisted language learning (CALL) tools or for grading candidates for formal qualifications. This thesis presents research conducted into the automatic assessment of spontaneous nonnative English speech, with a view to be able to provide meaningful feedback to learners. One of the challenges in automatic spoken language assessment is giving candidates feedback on particular aspects, or views, of their spoken language proficiency, in addition to the overall holistic score normally provided. Another is detecting pronunciation and other types of errors at the word or utterance level and feeding them back to the learner in a useful way. It is usually difficult to obtain accurate training data with separate scores for different views and, as examiners are often trained to give holistic grades, single-view scores can suffer issues of consistency. Conversely, holistic scores are available for various standard assessment tasks such as Linguaskill. An investigation is thus conducted into whether assessment scores linked to particular views of the speaker’s ability can be obtained from systems trained using only holistic scores. End-to-end neural systems are designed with structures and forms of input tuned to single views, specifically each of pronunciation, rhythm, intonation and text. By training each system on large quantities of candidate data, individual-view information should be possible to extract. The relationships between the predictions of each system are evaluated to examine whether they are, in fact, extracting different information about the speaker. Three methods of combining the systems to predict holistic score are investigated, namely averaging their predictions and concatenating and attending over their intermediate representations. The combined graders are compared to each other and to baseline approaches. The tasks of error detection and error tendency diagnosis become particularly challenging when the speech in question is spontaneous and particularly given the challenges posed by the inconsistency of human annotation of pronunciation errors. An approach to these tasks is presented by distinguishing between lexical errors, wherein the speaker does not know how a particular word is pronounced, and accent errors, wherein the candidate’s speech exhibits consistent patterns of phone substitution, deletion and insertion. Three annotated corpora x of non-native English speech by speakers of multiple L1s are analysed, the consistency of human annotation investigated and a method presented for detecting individual accent and lexical errors and diagnosing accent error tendencies at the speaker level

Automatic essay scoring for low level learners of English as a second language.

This thesis investigates the automatic assessment of essays written by Japanese low level learners of English as a second language. A number of essay features are investigated for their ability to predict human assessments of quality. These features include unique lexical signatures (Meara. Jacobs & Rodgers, 2002), distinctiveness, essay length, various measures of lexical diversity, mean sentence length and some properties of word distributions. Findings suggest that no one feature is sufficient to account for essay quality but essay length is a strong predictor for low level learners in time constrained tasks. Combinations of several features are much more powerful in predicting quality than single features. Some simple systems incorporating some of these features are also considered. One is a two-dimensional 'quantity/content' model based on essay length and lexical diversity. Various measures of lexical diversity are used for the content dimension. Another system considered is a clustering algorithm based on various lexical features. A third system is a Bayesian algorithm which classifies essays according to semantic content. Finally, an alternative process based on capture-recapture analysis is also considered for special cases of assessment. One interesting finding is that although many essay features only have moderate associations with quality, extreme values at both ends of the scale are often very reliable indicators of high quality' or poor quality essays. These easily identifiable high quality or low quality essays can act as training samples for classification algorithms such as Bayesian classifiers. The clustering algorithm used in this study correlated particularly strongly with human essay ratings. This suggests that multivariate statistical methods may help realise more accurate essay prediction