Inspector: The Tool For Automated Assessment Of Learner Text Complexity
EFL methodology has always recognized the importance of giving student learners of foreign languages regular and quick feedback on student speech production, both written and oral, and over the past two decades there appeared various tools for the provision of automated instant feedback. The presented paper offers an application that focuses on measuring text complexity, and the results are translated into feedback related to the author’s language proficiency. Along with some standard text complexity features, this tool takes into account those that are significant for Russian learners of English. The application provides students with advice on how to improve the weaker aspects of the evaluated essay by giving the statistics of the relevant linguistic features of the text in two different colours for the better and worse levels. We point out what text features are more relevant for the assessment of the essays written in English by Russian students. We analyzed 3440 texts from Russian Error-Annotated English Learner Corpus, and for each of them we calculated the text criteria values. Then we used the methods of machine learning and statistical analysis to predict the grade that could be received for the essay.
The conference was organised under the aegis of the Learner Corpus Association and was hosted by Eurac Research Institute for Applied Linguistics. It was themed "Widening the scope of learner corpus research" and brought together researchers and language teachers, software developers and linguists from 23 countries around the world.
In modern EFL teaching in Russia, much attention is paid to making students aware of variations in the cultural schemata represented by their L1 and the target language, as well as behavioral patterns of their speakers. At the same time, researchers and teaching practitioners scarcely address certain linguistic issues of Russian L1 prosodic interference that lead to attitudinal confusion on the part of native English speakers even when utterances produced by Russian EFL learners are void of any grammatical and lexical errors.
The study examines Russian L1 intonation in English, analyzes the differences in the pragmatic meaning created by the wrong application of L1 intonation contours, and looks at the reasons leading to the failure of the educational system to address the issue. Specifically, the paper investigates features of Russian L1 prosodic interference that affect communication and lead to misunderstanding of Russian speakers’ attitude or intent from native speakers’ perspective, as well as questions the importance of teacher beliefs in dealing with the problem. The results emphasize the importance of intonation teaching in an English classroom and suggest possible ways of dealing with institutional constraints that impede full-fledged intonation study.
The paper describes the learner corpus composed of English essays written by native Russian speakers. REALEC (Russian Error-Annotated Learner English Corpus) is an error-annotated, available online corpus, now containing more than 200 thousand word tokens in almost 800 essays. It is one of the first Russian ESL corpora, dynamically developing and striving to improve both in size and in features offered to users. We describe our perspective on the corpus, data sources and tools used in compiling it. Elaborate self-made classification of learners’ errors types is thoroughly described. The paper also presents a pilot experiment on creating test sets for particular learners’ problems using corpus data.
The project we present – Russian Learner Translator Corpus (RusLTC) is a multiple learner translator corpus which stores Russian students’ translations out of English and into it. The project is being developed by a cross-functional team of translator trainers and computational linguists in Russia. Translations are collected from several Russian universities; all translations are made as part of routine and exam assignments or as submissions for translation contests by students majoring in translation. As of March 2014 RusLTC contains the total of nearly 1.2 million word tokens, 258 source texts, and 1,795 translations. The paper gives a brief overview of the related research, describes the corpus structure and corpus-building technologies used; it also covers the query tool features and our error annotation solutions. In the final part we make a summary of the RusLTC-based research, its current practical applications and suggest research prospects and possibilities.
The paper discusses case (non-)coincidence in elliptical coordinated constructions, which is one of the most wide-spread type of errors that Russian native speaker make.
The workshop series on Natural Language Processing (NLP) for Computer-Assisted Language Learning (CALL) – NLP4CALL – is a meeting place for researchers working on the integration of Natural Language Processing and Speech Technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection.
The paper is focused on the study of reaction of italian literature critics on the publication of the Boris Pasternak's novel "Doctor Jivago". The analysys of the book ""Doctor Jivago", Pasternak, 1958, Italy" (published in Russian language in "Reka vremen", 2012, in Moscow) is given. The papers of italian writers, critics and historians of literature, who reacted immediately upon the publication of the novel (A. Moravia, I. Calvino, F.Fortini, C. Cassola, C. Salinari ecc.) are studied and analised.
In the article the patterns of the realization of emotional utterances in dialogic and monologic speech are described. The author pays special attention to the characteristic features of the speech of a speaker feeling psychic tension and to the compositional-pragmatic peculiarities of dialogic and monologic text.