通过后级蒸馏探索产品描述生成
Probing Product Description Generation via Posterior Distillation
摘要：
在产品描述生成（PDG）中，用户关心的方面是推荐系统的关键，它不仅可以改善用户的体验，而且可以获得更多的点击。高质量的客户评论可以被认为是挖掘用户关心的方面的理想来源。然而，在现实中，大量的新产品（称为长尾商品）无法收集足够数量的客户评论，这给产品描述生成任务带来了很大的挑战。现有的作品倾向于仅仅基于物品信息来生成产品描述，即，产品属性或标题词，导致内容单调乏味，不能有效吸引顾客。为了解决这个问题，我们提出了一个自适应后验网络的基础上Transformer架构，可以利用用户关心的信息，从客户评论。具体来说，我们首先扩展了自我关注的Transformer编码器来编码产品标题和属性。然后，我们应用自适应后蒸馏模块，利用有用的审查信息，它集成了用户关心的方面的生成过程。最后，我们应用了一个基于transformer的解码阶段与复制机制，自动生成产品描述。此外，我们还收集了大量的中文产品描述数据集，以支持我们在这一领域的工作和进一步的研究。实验结果表明，该模型在自动指标和人工评价方面均优于传统的生成式模型。
英文摘要：
In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks. High-quality customer reviews can be considered as an ideal source to mine user-cared aspects. However, in reality, a large number of new products (known as long-tailed commodities) cannot gather sufficient amount of customer reviews, which brings a big challenge in the product description generation task. Existing works tend to generate the product description solely based on item information, i.e., product attributes or title words, which leads to tedious contents and cannot attract customers effectively. To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews. Specifically, we first extend the self-attentive Transformer encoder to encode product titles and attributes. Then, we apply an adaptive posterior distillation module to utilize useful review information, which integrates user-cared aspects to the generation process. Finally, we apply a Transformer-based decoding phase with copy mechanism to automatically generate the product description. Besides, we also collect a large-scare Chinese product description dataset to support our work and further research in this field. Experimental results show that our model is superior to traditional generative models in both automatic indicators and human evaluation.
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。
