论文标题
使用转移学习和适配器的无监督神经风格文本生成
Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters
论文作者
论文摘要
研究表明，个性是提高参与度和用户体验在对话系统中的关键驱动力。对话代理还应保持一致的角色，以与用户进行引人入胜的对话。但是，文本生成数据集通常是人群来源的，因此具有平均效果，其中生成模型的样式是所有促成数据集的人群工人的平均风格。虽然可以为每个任务收集特定于角色的数据集，但这将是一项昂贵且耗时的注释工作。在这项工作中，我们提出了一个新颖的转移学习框架，该框架仅更新$ 0.3 \％的模型参数，以了解响应生成的样式特定属性。出于这项研究的目的，我们使用ROC Stories Copus解决了风格故事结尾的问题。我们从人格捕获数据集中学习样式特定的属性。通过广泛的实验和评估指标，我们表明，我们的新颖培训程序可以超过编码器 - 编码器基准，同时维持与PAR上的内容相关性指标，从而将风格的产生提高200
Research has shown that personality is a key driver to improve engagement and user experience in conversational systems. Conversational agents should also maintain a consistent persona to have an engaging conversation with a user. However, text generation datasets are often crowd sourced and thereby have an averaging effect where the style of the generation model is an average style of all the crowd workers that have contributed to the dataset. While one can collect persona-specific datasets for each task, it would be an expensive and time consuming annotation effort. In this work, we propose a novel transfer learning framework which updates only $0.3\%$ of model parameters to learn style specific attributes for response generation. For the purpose of this study, we tackle the problem of stylistic story ending generation using the ROC stories Corpus. We learn style specific attributes from the PERSONALITY-CAPTIONS dataset. Through extensive experiments and evaluation metrics we show that our novel training procedure can improve the style generation by 200 over Encoder-Decoder baselines while maintaining on-par content relevance metrics with
