从多个源检索：使用预训练的语言模型丰富维基数据的行补全
Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model
摘要：
行补全是用额外的相关行来扩充给定的文本和数字表的任务。该任务分为两个步骤：主题建议，填充主列的任务;和空白填充，填充其余列的任务。我们提出了国家的最先进的结果为主题的建议和差距填补衡量的标准基准（WikiTables）。我们的想法是通过和谐地结合知识库表解释和自由文本生成来解决这个任务。我们使用知识库来解释表，以建议新的行并通过属性链接生成像标题这样的元数据。为了提高候选人的多样性，我们通过GPT-3使用自由文本生成来合成额外的行，并且至关重要的是，我们利用我们解释的元数据来产生更好的文本生成提示。最后，我们验证了额外的合成内容可以链接到知识库或可信的网络源，如维基百科。
英文摘要：
Row completion is the task of augmenting a given table of text and numbers with additional, relevant rows. The task divides into two steps: subject suggestion, the task of populating the main column; and gap filling, the task of populating the remaining columns. We present state-of-the-art results for subject suggestion and gap filling measured on a standard benchmark (WikiTables). Our idea is to solve this task by harmoniously combining knowledge base table interpretation and free text generation. We interpret the table using the knowledge base to suggest new rows and generate metadata like headers through property linking. To improve candidate diversity, we synthesize additional rows using free text generation via GPT-3, and crucially, we exploit the metadata we interpret to produce better prompts for text generation. Finally, we verify that the additional synthesized content can be linked to the knowledge base or a trusted web source such as Wikipedia.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
