论文标题
将实体类型注入实体引导的文本生成
Injecting Entity Types into Entity-Guided Text Generation
论文作者
论文摘要
深度生成建模的最新成功导致了自然语言产生（NLG）的重大进展。通过协助推断摘要主题并产生连贯的内容，将实体纳入神经产生模型已显示出很大的改进。为了增强实体在NLG中的作用，在本文中，我们旨在在解码阶段对实体类型进行建模，以准确生成上下文单词。我们开发了一种新型的NLG模型，以根据给定的实体列表产生目标序列。我们的模型具有多步解码器，该解码器将实体类型注入实体提及生成过程中。两个公共新闻数据集的实验表明，类型注射的性能要比现有类型的嵌入串联基线表现更好。
Recent successes in deep generative modeling have led to significant advances in natural language generation (NLG). Incorporating entities into neural generation models has demonstrated great improvements by assisting to infer the summary topic and to generate coherent content. To enhance the role of entity in NLG, in this paper, we aim to model the entity type in the decoding phase to generate contextual words accurately. We develop a novel NLG model to produce a target sequence based on a given list of entities. Our model has a multi-step decoder that injects the entity types into the process of entity mention generation. Experiments on two public news datasets demonstrate type injection performs better than existing type embedding concatenation baselines.
