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.

本论文提出了一种基于广义跨语言转移的框架，可以增强模型理解不同语言的能力，通过不同语言组成多语言知识三元组，并通过链接预测技术设计一种知识注入策略，从而深入挖掘丰富的语义知识；实验结果表明，该方法可以大幅提高性能。