论文标题
摩卡咖啡：从认知的角度使用的多任务培训方法，用于连贯的文本生成
MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective
论文作者
论文摘要
教授神经模型来产生叙事相干文本是一个关键问题。最近的预训练的语言模型已取得了令人鼓舞的结果，但是人的书面文本和机器生成的输出之间仍然存在差距。在这项工作中，我们提出了一种基于写作认知理论的连贯文本生成的新型多任务培训策略，该策略使该模型能够学习写作所需的基本子技能，包括计划和审查端到端的一代。我们对三个开放式一代任务（包括故事生成，新闻文章写作和论点生成）的模型进行了广泛的评估。实验表明，我们的模型在几乎没有射击和全面监督的设置上取得更好的结果，而人类的评估则证实，我们的模型可以产生更多的相干输出。
Teaching neural models to generate narrative coherent texts is a critical problem. Recent pre-trained language models have achieved promising results, but there is still a gap between human written texts and machine-generated outputs. In this work, we propose a novel multi-task training strategy for coherent text generation grounded on the cognitive theory of writing, which empowers the model to learn essential subskills needed for writing including planning and reviewing besides end-to-end generation. We extensively evaluate our model on three open-ended generation tasks including story generation, news article writing and argument generation. Experiments show that our model achieves better results on both few-shot and fully-supervised settings than strong baselines, and human evaluations confirm that our model can generate more coherent outputs.
