mOKB6：一个多语言开放知识库补全基准
mOKB6: A Multilingual Open Knowledge Base Completion Benchmark
AI总结：
本文构建了首个包含六种语言的多语言开放知识库补全基准mOKB6，通过多语言共指消解和实体链接改进构建流程，并实验证明共享嵌入空间与事实翻译能一致提升补全性能，同时揭示现有多语言模型难以跨不同文字体系记忆事实。
摘要：
开放知识库（Open KBs）由通过开放信息抽取（Open IE）系统获得的三元组（主语短语，关系短语，宾语短语）构成，对其进行自动补全有助于发现文本中可能未直接出现的新事实。然而，开放知识库补全（Open KBC）的研究迄今仅限于英语等资源丰富的语言。利用多语言开放信息抽取的最新进展，我们构建了首个多语言开放知识库补全数据集，称为mOKB6，包含来自维基百科的六种语言（包括英语）的事实。通过进行多语言共指消解并仅保留实体链接三元组，我们改进了先前的开放知识库构建流程，创建了一个密集的开放知识库。我们针对该任务实验了多个模型，并观察到借助共享嵌入空间以及事实翻译来组合语言的一致收益。我们还观察到，当前的多语言模型难以记住以不同文字体系的语言所见到的事实。
英文摘要：
Automated completion of open knowledge bases (Open KBs), which are constructed from triples of the form (subject phrase, relation phrase, object phrase), obtained via open information extraction (Open IE) system, are useful for discovering novel facts that may not be directly present in the text. However, research in Open KB completion (Open KBC) has so far been limited to resource-rich languages like English. Using the latest advances in multilingual Open IE, we construct the first multilingual Open KBC dataset, called mOKB6, containing facts from Wikipedia in six languages (including English). Improving the previous Open KB construction pipeline by doing multilingual coreference resolution and keeping only entity-linked triples, we create a dense Open KB. We experiment with several models for the task and observe a consistent benefit of combining languages with the help of shared embedding space as well as translations of facts. We also observe that current multilingual models struggle to remember facts seen in languages of different scripts.
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。
