论文标题
模板可控关键字到文本生成
Template Controllable keywords-to-text Generation
论文作者
论文摘要
本文提出了一种新型的神经模型，用于从关键字中生成文本的研究。该模型作为输入一组未订购的关键字，以及基于语音的一部分（POS）模板指令。这使其成为任何NLG设置中表面实现的理想选择。该框架基于编码符号范式，其中关键字和模板首先是编码的，而解码器明智地通过从编码的关键字和模板得出的上下文中进行，以生成句子。培训利用了弱监督，因为该模型通过通过全自动手段制备的大量标记数据训练大量标记的数据。对各个领域公开可用的测试数据进行的定性和定量性能分析揭示了我们系统比基线的优越性，它是使用最先进的神经机器翻译和可控的转移技术构建的。我们的方法对输入关键字的顺序无动于衷。
This paper proposes a novel neural model for the understudied task of generating text from keywords. The model takes as input a set of un-ordered keywords, and part-of-speech (POS) based template instructions. This makes it ideal for surface realization in any NLG setup. The framework is based on the encode-attend-decode paradigm, where keywords and templates are encoded first, and the decoder judiciously attends over the contexts derived from the encoded keywords and templates to generate the sentences. Training exploits weak supervision, as the model trains on a large amount of labeled data with keywords and POS based templates prepared through completely automatic means. Qualitative and quantitative performance analyses on publicly available test-data in various domains reveal our system's superiority over baselines, built using state-of-the-art neural machine translation and controllable transfer techniques. Our approach is indifferent to the order of input keywords.
