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}.

OpenAI, the artificial intelligence research company founded by tech heavyweights including Elon Musk and Peter Thiel, says it's developed the most advanced language-processing algorithm so far. Sample outputs suggest that the AI system is an extraordinary step forward, producing text rich with context, nuance and even something approaching humor. It's so good, in fact, that OpenAI says it's not releasing its code to the public because its researchers are scared it could be misused, according to a new blog post. The algorithm, GPT-2, was trained on some 8 million web pages, according to the new research. Given a prompt, GPT-2 is tasked with predicting the next word based how those words have been used on the websites it read.