Machine-in-the-loop writing aims to enable humans to collaborate with models to complete their writing tasks more effectively. Prior work has found that providing humans a machine-written draft or sentence-level continuations has limited success since the generated text tends to deviate from humans' intention. To allow the user to retain control over the content, we train a rewriting model that, when prompted, modifies specified spans of text within the user's original draft to introduce descriptive and figurative elements locally in the text. We evaluate the model on its ability to collaborate with humans on the task of creative image captioning. On a user study through Amazon Mechanical Turk, our model is rated to be more helpful than a baseline infilling language model. In addition, third-party evaluation shows that users write more descriptive and figurative captions when collaborating with our model compared to completing the task alone.
翻译:机器在路边写作旨在使人类能够与模型合作,更有效地完成写作任务; 先前的工作发现,向人类提供机器起草的草稿或句级的延续成果有限,因为生成的文本往往偏离了人类的意图。 为了让用户保持对内容的控制,我们培训了一个重写模型,一旦触发,该模型将修改用户原始草稿中指定的文本范围,以在文本中引入描述性和比喻性的内容; 我们评估了模型在创造性的图像说明任务上与人类合作的能力。 在通过亚马逊机械土耳其语进行的用户研究中,我们的模型被评为比一个基线填充语言模型更有帮助。 此外,第三方评估显示,用户在与我们模型合作时,与仅仅完成任务相比,编写更多的描述性和比喻性说明性说明性说明性。