提升商品描述忠实度，让生成内容严格匹配产品属性
Stick to Facts: Towards Fidelity-oriented Product Description Generation
- 用实体标签引导的RNN解码器，让每个词生成时都参考其属性身份
- 引入关键词记忆库，通过实体标签精准调用对应关键词
- 在真实数据集上使描述忠实度提升25%，效果优于现有方法
与其它文本生成任务不同，商品描述生成中保持内容忠实于产品属性信息至关重要，但该问题关注较少。为填补空白，我们提出聚焦忠实度的商品描述生成模型FPDG。FPDG考虑每个词的实体标签，因产品属性信息通常由实体词传达。具体地，我们设计基于实体标签引导的长短期记忆（ELSTM）单元的循环神经网络（RNN）解码器，输入包括词嵌入和实体标签。其次，建立关键词记忆库，以实体标签为键、关键词为值，使模型可通过实体标签访问相关关键词。在大规模真实商品描述数据集上的实验表明，该模型在传统生成指标和人工评估上均达到领先水平，生成描述的忠实度提升了25%。
原文摘要 · Abstract (English)
Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information. However, little attention has been paid to this problem. To bridge this gap, we propose a model named Fidelity-oriented Product Description Generator (FPDG). FPDG takes the entity label of each word into account, since the product attribute information is always conveyed by entity words. Specifically, we first propose a Recurrent Neural Network (RNN) decoder based on the Entity-label-guided Long Short-Term Memory (ELSTM) cell, taking both the embedding and the entity label of each word as input. Second, we establish a keyword memory that stores the entity labels as keys and keywords as values, allowing FPDG to attend to keywords by attending to their entity labels. Experiments conducted on a large-scale real-world product description dataset show that our model achieves state-of-the-art performance in terms of both traditional generation metrics and human evaluations. Specifically, FPDG increases the fidelity of the generated descriptions by 25%.
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