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A Comparison of Neural Models for Word Ordering
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We compare several language models for the word-ordering task and propose a new bag-to-sequence neural model based on attention-based sequence-to-sequence models. We evaluate the model on a large German WMT data set where it significantly outperforms existing models. We also describe a novel search strategy for LM-based word ordering and report results on the English Penn Treebank. Our best model setup outperforms prior work both in terms of speed and quality.
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StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding
Adding word-order and sentence-order reconstruction tasks to BERT pre-training improves performance on GLUE, SNLI, and SQuAD v1.1 benchmarks.
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