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首页> 外文会议>International joint conference on natural language processing;Conference on empirical methods in natural language processing >(Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas
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(Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas

机译：（男，学士）和（女，博士学位）具有不同的含义：具有多个角色的平行注释的风格语言数据集

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Stylistic variation in text needs to be studied with different aspects including the writer's personal traits, interpersonal relations, rhetoric, and more. Despite recent attempts on computational modeling of the variation, the lack of parallel corpora of style language makes it difficult to systematically control the stylistic change as well as evaluate such models. We release PASTEL, the parallel and annotated stylistic language dataset. that contains ≈ 41K parallel sentences (8.3K parallel stories) annotated across different personas. Each persona has different styles in conjunction: gender, age, country, political view, education, ethnic, and time-of-writing. The dataset is collected from human annotators with solid control of input denotation: not only preserving original meaning between text, but promoting stylistic diversity to annotators. We test the dataset on two interesting applications of style language, where PASTEL helps design appropriate experiment and evaluation. First, in predicting a target style (e.g., male or female in gender) given a text, multiple styles of PASTEL make other external style variables controlled (or fixed), which is a more accurate experimental design. Second, a simple supervised model with our parallel text outperforms the unsupervised models using non-parallel text in style transfer. Our dataset is publicly available~1.
机译：需要从不同方面研究文本的风格变化，包括作者的个人特征，人际关系，修辞等等。尽管最近对变体的计算建模进行了尝试，但是缺乏样式语言的并行语料库使得难以系统地控制样式变化以及评估此类模型。我们发布了PASTEL，这是一种并行且带注释的风格语言数据集。包含≈41K平行句子（8.3K平行故事），并在不同角色间进行了注释。每个角色在风格上都有不同的风格：性别，年龄，国家，政治观点，教育程度，种族和写作时间。该数据集是从人类注释者那里收集的，并可靠地控制了输入注释：不仅保留了文本之间的原始含义，而且还促进了注释者的风格多样性。我们在两种有趣的样式语言应用程序上测试数据集，其中PASTEL帮助设计适当的实验和评估。首先，在预测给定文本的目标样式（例如性别中的男性或女性）时，PASTEL的多种样式可以控制（或固定）其他外部样式变量，这是一种更为准确的实验设计。其次，带有并行文本的简单监督模型在样式转换中优于使用非并行文本的无监督模型。我们的数据集是公开可用的〜1。

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