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Register a new userSolving SCAN Tasks with Data Augmentation and Input Embeddings
حل المهام المسح الضوئي مع زيادة البيانات وإدخال المدخلات
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نحن نتطلع إلى تحدي التركيب المقدم من مؤشر المسح.باستخدام تكبير البيانات وتعديل هندسة SEQ2SEQ القياسية مع الاهتمام، نحقق نتائج SOTA على جميع المهام ذات الصلة من المعيار، وإظهار أن النماذج يمكن أن تعميم الكلمات المستخدمة في السياقات غير المرئية.نقترح امتدادا للمعيار من خلال مهمة أصعب، والتي لا يمكن حلها بالطريقة المقترحة.
We address the compositionality challenge presented by the SCAN benchmark. Using data augmentation and a modification of the standard seq2seq architecture with attention, we achieve SOTA results on all the relevant tasks from the benchmark, showing the models can generalize to words used in unseen contexts. We propose an extension of the benchmark by a harder task, which cannot be solved by the proposed method.
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Unsupervised Data Augmentation (UDA) is a semisupervised technique that applies a consistency loss to penalize differences between a model's predictions on (a) observed (unlabeled) examples; and (b) corresponding noised' examples produced via data au
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