From input to output: the potential of parallel corpora for CALL

Abstract

The aim of this paper is to illustrate the potential of a parallel corpus in the context of (computer-assisted) language learning. In order to do so, we propose to answer two main questions (1) what corpus (data) to use and (2) how to use the corpus (data). We provide an answer to the what-question by describing the importance and particularities of compiling and processing a corpus for pedagogical purposes. In order to answer the how-question, we first investigate the central concepts of the interactionist theory of second language acquisition: comprehensible input, input enhancement, comprehensible output and output enhancement. By means of two case studies, we illustrate how the abovementioned concepts can be realized in concrete corpus-based language learning activities. We propose a design for a receptive and productive language task and describe how a parallel corpus can be at the basis of powerful language learning activities. The Dutch Parallel Corpus, a ten-million word sentence aligned and annotated parallel corpus, is used to develop these language tasks

Dutch Parallel Corpus: A Balanced Parallel Corpus for Dutch-English and Dutch-French

“…The corpus is being used as a sample repository for content developers preparing exercises for French and Dutch language learners [9]. Within CorpusCALL, parallel corpora like DPC are used as resources for datadriven language learning [20]. Parallel corpora are also useful instruments for rethinking the pedagogical grammaticography in function of frequency research.…”

Corpus-based learning of Cantonese for Mandarin speakers

“…Due to the cost of manual sentence alignment, there is usually a trade-off between large corpus size and automatic alignment (Gao, 2011; Montero Perez et al ., 2014). The source sentences of this corpus, fortunately, came already aligned: the Mandarin subtitles were aligned to the simultaneously spoken Cantonese.…”

“…In these learning tasks, students typically formulate linguistic hypotheses about some aspects of their second language (L2), then confirm or reject them based on evidence observed in the corpus (Nerbonne, 2000; Montero Perez, Paulussen, Macken & Desmet, 2014); they play, in effect, the role of a linguistic researcher or a detective (Johns, 1997: 101). Because it is so demanding on students, DDL or corpus-based language learning has been attempted mostly with intermediate to advanced level students.…”

Learning languages from parallel corpora

“…Those patterns can relate to any linguistic level, such as lexicon, morphology, or syntax. While the idea of learning languages utilizing language material (as opposed to learning by prescribed rules) has been around for several decades, and its efficacy has been experimentally substantiated, the use of parallel corpora for that purpose has received significantly less attention (Lawson, 2001;Bluemel, 2014;Montero Perez et al 2014, to name a few).…”