弥合语言鸿沟：知识注入的多语言问答
Bridging the Language Gap: Knowledge Injected Multilingual Question Answering
AI总结：
本文提出一种广义跨语言迁移框架，通过组装多语言知识三元组并利用链接预测技术进行知识注入，增强模型跨语言理解与推理能力，在MLQA数据集上F1/EM平均提升13.18%/12.00%。
摘要：
问答（QA）是自动回答人类用自然语言提出的问题的任务。回答问题有不同的设置，如生成式、抽取式、布尔式和多项选择式问答。作为自然语言处理任务中的热门主题，抽取式问答任务（extractive QA）在过去几年中获得了广泛关注。随着世界的不断发展，广义跨语言迁移（G-XLT），即问题和答案上下文处于不同语言中，对跨语言迁移（XLT），即问题和答案上下文处于同一种语言中，提出了一些独特的挑战。随着相关基准的相应发展，许多工作致力于提高各种语言问答任务的性能。然而，只有少数工作致力于G-XLT任务。在这项工作中，我们提出了一个广义跨语言迁移框架，以增强模型理解不同语言的能力。具体来说，我们首先从不同语言中组装三元组以形成多语言知识。由于不同语言之间知识的缺乏极大地限制了模型的推理能力，我们进一步设计了一种知识注入策略，通过利用链接预测技术来丰富模型的多语言知识存储。通过这种方式，我们可以深入挖掘丰富的语义知识。在真实世界数据集MLQA上的实验结果表明，所提出的方法可以大幅提高性能，平均超过基线方法13.18%/12.00%的F1/EM。
英文摘要：
Question Answering (QA) is the task of automatically answering questions posed by humans in natural languages. There are different settings to answer a question, such as abstractive, extractive, boolean, and multiple-choice QA. As a popular topic in natural language processing tasks, extractive question answering task (extractive QA) has gained extensive attention in the past few years. With the continuous evolvement of the world, generalized cross-lingual transfer (G-XLT), where question and answer context are in different languages, poses some unique challenges over cross-lingual transfer (XLT), where question and answer context are in the same language. With the boost of corresponding development of related benchmarks, many works have been done to improve the performance of various language QA tasks. However, only a few works are dedicated to the G-XLT task. In this work, we propose a generalized cross-lingual transfer framework to enhance the model's ability to understand different languages. Specifically, we first assemble triples from different languages to form multilingual knowledge. Since the lack of knowledge between different languages greatly limits models' reasoning ability, we further design a knowledge injection strategy via leveraging link prediction techniques to enrich the model storage of multilingual knowledge. In this way, we can profoundly exploit rich semantic knowledge. Experiment results on real-world datasets MLQA demonstrate that the proposed method can improve the performance by a large margin, outperforming the baseline method by 13.18%/12.00% F1/EM on average.
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。
