Building a Parallel Corpus on the World's Oldest Banking Magazine

Amrhein, Chantal; Aepli, Noëmi; Müller, Mathias; Ströbel, Phillip

doi:10.5167/uzh-125746

Cited by 2 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of commonly used tools and methods exist to improve alignment performance (e.g. Varga et al, 2005;Braune and Fraser, 2010;Sennrich and Volk, 2010), and new methods keep being developed (Thompson and Koehn, 2019;Jiang et al, 2020).…”

Section: Alignmentmentioning

confidence: 99%

Learning languages from parallel corpora

Graën

2022

SLO2.0

View full text Add to dashboard Cite

This work describes a blueprint for an application that generates language learning exercises from parallel corpora. Word alignment and parallel structures allow for the automatic assessment of sentence pairs in the source and target languages, while users of the application continuously improve the quality of the data with their interactions, thus crowdsourcing parallel language learning material. Through triangulation, their assessment can be transferred to language pairs other than the original ones if multiparallel corpora are used as a source. Several challenges need to be addressed for such an application to work, and we will discuss three of them here. First, the question of how adequate learning material can be identified in corpora has received some attention in the last decade, and we will detail what the structure of parallel corpora implies for that selection. Secondly, we will consider which type of exercises can be generated automatically from parallel corpora such that they foster learning and keep learners motivated. And thirdly, we will highlight the potential of employing users, that is both teachers and learners, as crowdsourcers to help improve the material.

show abstract

Section: Alignmentmentioning

confidence: 99%