Elena Volodina

Corpus-based vocabulary lists for language learners for nine languages

We present the KELLY project and its work on developing monolingual and bilingual word lists for language learning, using corpus methods, for nine languages and thirty-six language pairs. We describe the method and discuss the many challenges encountered. We have loaded the data into an online database to make it accessible for anyone to explore and we present our own first explorations of it. The focus of the paper is thus twofold, covering pedagogical and methodological aspects of the lists’ construction, and linguistic aspects of the by-product of the project, the KELLY database.

Rule-based and machine learning approaches for second language sentence-level readability

We present approaches for the identification of sentences understandable by second language learners of Swedish, which can be used in automatically generated exercises based on corpora. In this work we merged methods and knowledge from machine learning-based readability research, from rule-based studies of Good Dictionary Examples and from second language learning syllabuses. The proposed selection methods have also been implemented as a module in a free web-based language learning platform. Users can use different parameters and linguistic filters to personalize their sentence search with or without a machine learning component assessing readability. The sentences selected have already found practical use as multiple-choice exercise items within the same platform. Out of a number of deep linguistic indicators explored, we found mainly lexical-morphological and semantic features informative for second language sentence-level readability. We obtained a readability classification accuracy result of 71%, which approaches the performance of other models used in similar tasks. Furthermore, during an empirical evaluation with teachers and students, about seven out of ten sentences selected were considered understandable, the rulebased approach slightly outperforming the method incorporating the machine learning model.

A Report on the Automatic Evaluation of Scientific Writing Shared Task.

The Automated Evaluation of Scientific Writing, or AESW, is the task of identifying sentences in need of correction to ensure their appropriateness in a scientific prose. The data set comes from a professional editing company, VTeX, with two aligned versions of the same text – before and after editing – and covers a variety of textual infelicities that proofreaders have edited. While previous shared tasks focused solely on grammatical errors (Dale and Kilgarriff, 2011; Dale et al., 2012; Ng et al., 2013; Ng et al., 2014), this time edits cover other types of linguistic misfits as well, including those that almost certainly could be interpreted as style issues and similar “matters of opinion”. The latter arise because of different language editing traditions, experience, and the absence of uniform agreement on what “good” scientific language should look like. Initiating this task, we expected the participating teams to help identify the characteristics of “good” scientific language, and help create a consensus of which language improvements are acceptable (or necessary). Six participating teams took on the challenge.

Lark Trills for Language Drills: Text-to-speech technology for language learners

This paper reports on the development and the initial evaluation of a dictation&spelling prototype exercise for second language (L2) learners of Swedish based on text-to-speech (TTS) technology. Implemented on an already existing Intelligent Computer-Assisted Language Learning (ICALL) platform, the exercise has not only served as a test case for TTS in L2 environment, but has also shown a potential to train listening and orthographic skills, as well as has become a way of collecting learner-specific spelling errors into a database. Exercise generation re-uses well-annotated corpora, lexical resources, and text-to-speech technology with an accompanying talking head.