FANS：用于物品列表续写的快速非自回归序列生成
FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation
AI总结：
提出非自回归序列生成模型FANS，通过同时解码和两阶段分类器提升推理效率，结合课程学习优化质量，在多个数据集上实现最高8.7倍加速且保持生成质量。
摘要：
用户策划的物品列表，如YouTube上的视频播放列表和Goodreads上的书籍列表，在在线平台的内容分享中已变得非常普遍。物品列表续写被提出来用于建模列表的整体趋势并预测后续物品。最近，基于Transformer的模型在理解上下文信息和捕捉列表中物品关系方面展现出了潜力。然而，将它们部署在实时工业应用中具有挑战性，主要是因为它们使用的自回归生成机制非常耗时。在本文中，我们提出了一种新颖的快速非自回归序列生成模型，即FANS，以提高物品列表续写的推理效率和质量。首先，我们使用非自回归生成机制同时解码接下来的$K$个物品，而不是像现有模型那样逐个解码。然后，我们设计了一个两阶段分类器来替代当前基于Transformer的模型中使用的普通分类器，以进一步减少解码时间。此外，为了提高非自回归生成的质量，我们采用了课程学习策略来优化训练。在四个真实世界物品列表续写数据集（包括Zhihu、Spotify、AotM和Goodreads）上的实验结果表明，与最先进的自回归模型相比，我们的FANS模型能够显著提高推理效率（高达8.7倍），同时在物品列表续写方面实现具有竞争力或更好的生成质量。我们还在工业环境中验证了FANS的效率。我们的源代码和数据将在MindSpore/models和Github上提供。
英文摘要：
User-curated item lists, such as video-based playlists on Youtube and book-based lists on Goodreads, have become prevalent for content sharing on online platforms. Item list continuation is proposed to model the overall trend of a list and predict subsequent items. Recently, Transformer-based models have shown promise in comprehending contextual information and capturing item relationships in a list. However, deploying them in real-time industrial applications is challenging, mainly because the autoregressive generation mechanism used in them is time-consuming. In this paper, we propose a novel fast non-autoregressive sequence generation model, namely FANS, to enhance inference efficiency and quality for item list continuation. First, we use a non-autoregressive generation mechanism to decode next $K$ items simultaneously instead of one by one in existing models. Then, we design a two-stage classifier to replace the vanilla classifier used in current transformer-based models to further reduce the decoding time. Moreover, to improve the quality of non-autoregressive generation, we employ a curriculum learning strategy to optimize training. Experimental results on four real-world item list continuation datasets including Zhihu, Spotify, AotM, and Goodreads show that our FANS model can significantly improve inference efficiency (up to 8.7x) while achieving competitive or better generation quality for item list continuation compared with the state-of-the-art autoregressive models. We also validate the efficiency of FANS in an industrial setting. Our source code and data will be available at MindSpore/models and Github.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
