%0 Conference Proceedings
%T Discussion on Bilingual Cognition in International Exchange Activities
%+ Tsinghua University [Beijing] (THU)
%+ Sino-American Saerle Research Center
%A Maimaiti, Mieradilijiang
%A Zou, Xiaohui
%Z Part 4: Language Cognition
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 2nd International Conference on Intelligence Science (ICIS)
%C Beijing, China
%Y Zhongzhi Shi
%Y Cyriel Pennartz
%Y Tiejun Huang
%I Springer International Publishing
%3 Intelligence Science II
%V AICT-539
%P 167-177
%8 2018-11-02
%D 2018
%R 10.1007/978-3-030-01313-4_17
%K Bilingual cognition
%K International exchange activities
%K Human-computer interaction
%K Neural machine translation
%K Transfer learning
%Z Computer Science [cs]Conference papers
%X This article aims to explore the features, mechanisms, and applications of bilingual cognition in international communication activities. Our main idea is: First, clarify the mother tongue of each international exchange activities (IEAs) and prepare some prerequisites which are related to the discussion. Then, make full use of the information and intelligent network tools to bring out the subjective initiative of both parties, while conducting the corresponding research on daily terms and professional terms, and generate the two series of bilingual phrase table. Finally, use the machine translation (MT) and translation memory tools to help them make the necessary preparations or exercises. Meanwhile, we propose the novel and efficient mixed transfer learning (MTL) approach. As a result, when the two parties communicate with each other, as well as via online or off-line communicate, the kind of tacit agreement would have been created between them. If so, it will have been leveraged among them repeatedly rather than just one time and will have targeted multiple times. Its significance lies in: This process and habit of human-computer interaction will better reveal the characteristics of bilingual cognition based on this article. Experiments on low-resource datasets show that our approach is effective, significantly outperform the state-of-the-art methods and yield improvements of up to 4.13 BLEU points.
%G English
%Z TC 12
%2 https://inria.hal.science/hal-02118842/document
%2 https://inria.hal.science/hal-02118842/file/474230_1_En_17_Chapter.pdf
%L hal-02118842
%U https://inria.hal.science/hal-02118842
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC12
%~ IFIP-ICIS
%~ IFIP-AICT-539