Web: http://arxiv.org/abs/2209.01765

Sept. 19, 2022, 1:15 a.m. | Xiaodong Gu, Zhaowei Zhang, Sang-Woo Lee, Kang Min Yoo, Jung-Woo Ha

cs.CL updates on arXiv.org arxiv.org

While Transformers have had significant success in paragraph generation, they
treat sentences as linear sequences of tokens and often neglect their
hierarchical information. Prior work has shown that decomposing the levels of
granularity~(e.g., word, phrase, or sentence) for input tokens has produced
substantial improvements, suggesting the possibility of enhancing Transformers
via more fine-grained modeling of granularity. In this work, we propose a
continuous decomposition of granularity for neural paraphrase generation
(C-DNPG). In order to efficiently incorporate granularity into sentence
encoding, …

arxiv continuous

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