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Solving SCAN Tasks with Data Augmentation and Input Embeddings

حل المهام المسح الضوئي مع زيادة البيانات وإدخال المدخلات

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 Publication date 2021
and research's language is English
 Created by Shamra Editor




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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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