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Modeling Noise in Paraphrase Detection
Noisy labels in training data present a challenging issue in classification tasks, misleading a model towards incorrect decisions during training.
Coping with Noisy Training Data Labels in Paraphrase Detection
We present new state-of-the-art benchmarks for paraphrase detection on all six languages in the Opusparcus sentential paraphrase corpus: English, Finnish, French, German, Russian, and Swedish.
Grammatical Error Generation Based on Translated Fragments
We perform neural machine translation of sentence fragments in order to create large amounts of training data for English grammatical error correction.
Paraphrase Detection on Noisy Subtitles in Six Languages
We perform automatic paraphrase detection on subtitle data from the Opusparcus corpus comprising six European languages: German, English, Finnish, French, Russian, and Swedish.
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