Hsu, T.-Y.T.-Y.HsuLiu, C.-L.C.-L.LiuHUNG-YI LEE2021-05-052021-05-052020https://www.scopus.com/inward/record.url?eid=2-s2.0-85084292524&partnerID=40&md5=dff21119d1e1ef845a97d7e0b9215b07https://scholars.lib.ntu.edu.tw/handle/123456789/558962Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning. In this paper, we systematically explore zero-shot cross-lingual transfer learning on reading comprehension tasks with a language representation model pre-trained on multi-lingual corpus. The experimental results show that with pre-trained language representation zero-shot learning is feasible, and translating the source data into the target language is not necessary and even degrades the performance. We further explore what does the model learn in zero-shot setting0. © 2019 Association for Computational LinguisticsLearning systems; Linguistics; Natural language processing systems; Cross-lingual; Reading comprehension; Representation model; Source data; Target language; Training data; Transfer learningZero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Modelconference paper2-s2.0-85084292524