Example Retrieval System using Grammatical Error Detection for Japanese as a Second Language Learners

Example sentences retrieval systems display examples that match the input query. They help language learner’swriting. Learners can use the pattern of the examples in their writing. If the query word is correct, learners can findexamples in a native corpus. However, if the query word is incorrect, it...

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Veröffentlicht in:Transactions of the Japanese Society for Artificial Intelligence 2020/09/01, Vol.35(5), pp.A-K23_1-9
Hauptverfasser: Arai, Mio, Kaneko, Masahiro, Komachi, Mamoru
Format: Artikel
Sprache:eng ; jpn
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Zusammenfassung:Example sentences retrieval systems display examples that match the input query. They help language learner’swriting. Learners can use the pattern of the examples in their writing. If the query word is correct, learners can findexamples in a native corpus. However, if the query word is incorrect, it is impossible to find appropriate examplesusing ordinary search engines. Existing example retrieval systems do not include grammatically incorrect examples,or only present a few examples, if any. Even if a retrieval system has a wide coverage of incorrect examples alongwith the correct counterparts, learners need to know whether their query includes errors. Considering the usability ofretrieving incorrect examples, our proposed method uses a large-scale learner corpus and presents correct expressionsalong with incorrect expressions using a grammatical error detection system so that the learner does not need to beaware of how to search for examples. Learners can recognize the wrong part in the input query and know how torevise the wrong part when they check correct expressions along with incorrect expressions. Evaluations indicate thatour method improves the accuracy of example sentence retrieval and the quality of a learner’s writing. To the bestof our knowledge, our system is the first incorrect example sentence retrieval system using neural grammatical errordetection.
ISSN:1346-0714
1346-8030
DOI:10.1527/tjsai.35-5_A-K23