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 \textit{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: \url{https://github.com/steven640pixel/nonautoregressive-sentence-ordering}.

翻译：现有句子排序方法通常采用基于指针网络的编码器-解码器框架，通过逐步循环预测每个句子的方式来恢复文本连贯性。这种自回归解码方式仅利用单向依赖关系，无法充分探索句子间的语义依存关系。为解决这一局限，本文提出新型非自回归排序网络NAON（Non-Autoregressive Ordering Network），可并行探索句子间的双向依赖关系并同步预测每个位置的句子。我们主张非自回归范式不仅适用于句子排序任务，更具备独特优势，这源于该任务的两个特性：1）每个生成目标具有确定性长度，2）句子与位置需保持一一对应关系。此外，为缓解朴素非自回归Transformer的重复生成问题，我们引入排他性损失函数对位置与句子间的互斥关系进行约束。通过在多个常用数据集上的广泛实验，验证了所提模型的有效性，实验结果表明本方法全面超越各类自回归方法，且性能与当前最先进模型持平。相关代码已开源至：\url{https://github.com/steven640pixel/nonautoregressive-sentence-ordering}