Non-Autoregressive Sentence Ordering
Yi Bin, Wenhao Shi, Bin Ji, Jipeng Zhang, Yujuan Ding, Yang Yang · 2023
Existing sentence ordering approaches generally employ encoder-decoder frameworks with the pointer net to recover the coherence by recurrently predicting each sentence step-by-step.Such an autoregressive manner only leverages unilateral dependencies during decoding and cannot fully explore the semantic dependency between sentences for ordering.To overcome these limitations, in this paper, we propose a novel Non-Autoregressive Ordering Network, dubbed NAON, which explores bilateral dependencies between sentences and predicts the sentence for each position in parallel.We claim that the non-autoregressive manner is not just applicable but also particularly suitable to the sentence ordering task because of two peculiar characteristics of the task: 1) each generation target is in deterministic length, and 2) the sentences and positions should match exclusively.Furthermore, to address the repetition issue of the naive non-autoregressive Transformer, we introduce an exclusive loss to constrain the exclusiveness between positions and sentences.To verify the effectiveness of the proposed model, we conduct extensive experiments on several common-used datasets and the experimental results show that our method outperforms all the autoregressive approaches and yields competitive performance compared with the state-of-the-arts.The codes are available at: https://github.com/steven640pixel/ nonautoregressive-sentence-ordering.