Our experiments show that the inclusion of artificially generated errors significantly improves error detection accuracy on both FCE and CoNLL 2014 datasets.
Jul 17, 2017 · This paper investigates two alternative methods for artificially generating writing errors, in order to create additional resources.
We propose a framework for generat- ing errors based on statistical machine translation. (SMT), training a model to translate from correct into incorrect ...
Jul 17, 2017 · This paper investigates two alternative methods for artificially generating writing errors, in order to create additional resources. We propose ...
In the educational field, [9, 10] augmented training data by generating synthetic errors using neural models trained on a small corpus of humanannotated data.
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The first approach uses regular machine translation, essentially translating from correct English to incorrect English. The second method uses local patterns ...
Artificial Error Generation with Machine Translation and Syntactic Patterns · Computer Science, Linguistics. BEA@EMNLP · 2017.
Artificial Error Generation with Machine Translation and Syntactic Patterns ... Generating artificial errors for grammatical error correction · no code ...
Jul 21, 2019 · Artificial error generation with · machine translation and syntactic patterns. In. Proceedings of the 12th Workshop on Innovative. Use of NLP ...
Artificial error generation for translation-based grammatical error correction ... Artificial Error Generation with Machine Translation and Syntactic Patterns.