DOCSLIB.ORG

Multilingual Extractive Reading Comprehension by Runtime Machine Translation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Multilingual Extractive Reading Comprehension by Runtime Machine Translation Multilingual Extractive Reading Comprehension by Runtime Machine Translation Akari Asaiy, Akiko Eriguchiy, Kazuma Hashimotoz, and Yoshimasa Tsuruokay yThe University of Tokyo zSalesforce Research [email protected] zferiguchi,[email protected] [email protected] Abstract to English. To alleviate the scarcity of training data in non- Despite recent work in Reading Comprehen- English languages, previous work creates a new sion (RC), progress has been mostly limited to English due to the lack of large-scale datasets large-scale dataset for a language of interest (He in other languages. In this work, we introduce et al., 2017) or combines a medium-scale dataset the first RC system for languages without RC in the language with an existing dataset translated training data. Given a target language without from English (Lee et al., 2018). These efforts in RC training data and a pivot language with RC data creation are often costly, and must be repeated training data (e.g. English), our method lever- for each new language of interest. In addition, they ages existing RC resources in the pivot lan- do not leverage existing resources in English RC, guage by combining a competitive RC model such as the wealth of large-scale datasets and state- in the pivot language with an attentive Neural Machine Translation (NMT) model. We first of-the-art models. translate the data from the target to the pivot In this paper, we propose a multilingual extrac- language, and then obtain an answer using the tive RC method by runtime Machine Translation RC model in the pivot language. Finally, we (MT), a new method for building RC systems for recover the corresponding answer in the orig- languages without RC training data. Our method inal language using soft-alignment attention combines an RC model with a Neural Machine scores from the NMT model. We create eval- Translation model (NMT). Given a language L of uation sets of RC data in two non-English lan- guages, namely Japanese and French, to evalu- interest with no RC data and a pivot language P ate our method. Experimental results on these with large-scale RC training data, we first trans- datasets show that our method significantly late a document context and question from the outperforms a back-translation baseline of a language L to the language P using an attentive state-of-the-art product-level machine transla- NMT model. Next, we obtain an answer from the tion system. RC model in language P . Finally, we recover the answer in the context in language L using soft- 1 Introduction alignments from the NMT model. Extractive Reading Comprehension (RC), in To our knowledge, our work is the first method which a model identifies the answer to a given that requires no RC training data in the target lan- arXiv:1809.03275v2 [cs.CL] 2 Nov 2018 question from a document context by “extracting” guage to build an RC model for the language of the correct answer, has a variety of downstream interest. In contrast to existing work on RC in applications such as search, automated FAQs, and non-English languages, our method leverages ex- dialogue systems. Recent years have seen rapid isting work in English RC. More importantly, our progress in the development of RC models (Seo method requires no additional annotation effort to et al., 2017; Wang et al., 2017; Xiong et al., acquire RC data in the target language. 2018; Yu et al., 2018; Hu et al., 2018) due to the To demonstrate the effectiveness of our method, availability of large-scale annotated corpora (Her- we focus on SQuAD, one of the most widely-used mann et al., 2015; Rajpurkar et al., 2016; Joshi large-scale English datasets for extractive RC, and et al., 2017). However, these large-scale annotated created SQuAD test data in Japanese and French. datasets are often exclusive to English. Conse- On Japanese and French SQuAD, our method sig- quently, progress in RC has been largely limited nificantly outperforms a back-translation baseline 1 ↵ij <latexit sha1_base64="5/UoLW5mpMtRvo3dkuIh3phUGJI=">AAAB8nicbVBNS8NAEJ3Ur1q/qh69LBbBU0mkoMeCF48V7AekoWy2m3btJht2J0IJ/RlePCji1V/jzX/jts1BWx8MPN6bYWZemEph0HW/ndLG5tb2Tnm3srd/cHhUPT7pGJVpxttMSaV7ITVcioS3UaDkvVRzGoeSd8PJ7dzvPnFthEoecJryIKajRESCUbSS36cyHdNBLh5ng2rNrbsLkHXiFaQGBVqD6ld/qFgW8wSZpMb4nptikFONgkk+q/Qzw1PKJnTEfUsTGnMT5IuTZ+TCKkMSKW0rQbJQf0/kNDZmGoe2M6Y4NqveXPzP8zOMboJcJGmGPGHLRVEmCSoy/58MheYM5dQSyrSwtxI2ppoytClVbAje6svrpHNV99y6d9+oNRtFHGU4g3O4BA+uoQl30II2MFDwDK/w5qDz4rw7H8vWklPMnMIfOJ8/l/yRZg==</latexit> attention scores Question in Question in Q<latexit sha1_base64="G4uyUPFEK2RZkYw5m8lY9ibL7o0=">AAAB6nicbVA9SwNBEJ2LXzF+RS1tFoNgFe5EiGXAxsIiQfMByRH2NnPJkr29Y3dPCEd+go2FIrb+Ijv/jZvkCk18MPB4b4aZeUEiuDau++0UNja3tneKu6W9/YPDo/LxSVvHqWLYYrGIVTegGgWX2DLcCOwmCmkUCOwEk9u533lCpXksH800QT+iI8lDzqix0kNzcD8oV9yquwBZJ15OKpCjMSh/9YcxSyOUhgmqdc9zE+NnVBnOBM5K/VRjQtmEjrBnqaQRaj9bnDojF1YZkjBWtqQhC/X3REYjradRYDsjasZ61ZuL/3m91IQ3fsZlkhqUbLkoTAUxMZn/TYZcITNiagllittbCRtTRZmx6ZRsCN7qy+ukfVX13KrXvK7U3TyOIpzBOVyCBzWowx00oAUMRvAMr/DmCOfFeXc+lq0FJ585hT9wPn8A87mNgg==</latexit> L Q<latexit sha1_base64="RSRwn1mCyIlfeVM/dbvfSx7UNmU=">AAAB6nicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLgxmWL9gFtKJPppB06mYSZG6GEfoIbF4q49Yvc+TdO2iy09cDA4ZxzmXtPkEhh0HW/ndLG5tb2Tnm3srd/cHhUPT7pmDjVjLdZLGPdC6jhUijeRoGS9xLNaRRI3g2md7nffeLaiFg94izhfkTHSoSCUbTSQ2vYHFZrbt1dgKwTryA1KGDzX4NRzNKIK2SSGtP33AT9jGoUTPJ5ZZAanlA2pWPet1TRiBs/W6w6JxdWGZEw1vYpJAv190RGI2NmUWCTEcWJWfVy8T+vn2J462dCJSlyxZYfhakkGJP8bjISmjOUM0so08LuStiEasrQtlOxJXirJ6+TzlXdc+te67rWcIs6ynAG53AJHtxAA+6hCW1gMIZneIU3RzovzrvzsYyWnGLmFP7A+fwB+cmNhg==</latexit> P Target Pivot Language Attentive Neural Language Extractive (s, e) Machine Reading <latexit sha1_base64="eeNAar+nRdJpWZbzhkYyorIxHMs=">AAAB7XicbVBNS8NAEJ3Ur1q/qh69LBahgpREBD0WvHisYD+gDWWznbRrN5uwuxFK6H/w4kERr/4fb/4bt20O2vpg4PHeDDPzgkRwbVz32ymsrW9sbhW3Szu7e/sH5cOjlo5TxbDJYhGrTkA1Ci6xabgR2EkU0igQ2A7GtzO//YRK81g+mEmCfkSHkoecUWOlVlVfEDzvlytuzZ2DrBIvJxXI0eiXv3qDmKURSsME1brruYnxM6oMZwKnpV6qMaFsTIfYtVTSCLWfza+dkjOrDEgYK1vSkLn6eyKjkdaTKLCdETUjvezNxP+8bmrCGz/jMkkNSrZYFKaCmJjMXicDrpAZMbGEMsXtrYSNqKLM2IBKNgRv+eVV0rqseW7Nu7+q1N08jiKcwClUwYNrqMMdNKAJDB7hGV7hzYmdF+fd+Vi0Fpx85hj+wPn8ARy1jhk=</latexit> Phrase (s ,e ) <latexit sha1_base64="e+6EfY7xsP3THE+vwa0Q/pUVkBo=">AAAB8XicbVDLSgNBEOyNrxhfUY9eBoMQQcKuCHoMePGQQwTzwGRZZiedZMjs7DIzK4SQv/DiQRGv/o03/8ZJsgdNLGgoqrrp7goTwbVx3W8nt7a+sbmV3y7s7O7tHxQPj5o6ThXDBotFrNoh1Si4xIbhRmA7UUijUGArHN3O/NYTKs1j+WDGCfoRHUje54waKz2WdVC7IBjUzoNiya24c5BV4mWkBBnqQfGr24tZGqE0TFCtO56bGH9CleFM4LTQTTUmlI3oADuWShqh9ifzi6fkzCo90o+VLWnIXP09MaGR1uMotJ0RNUO97M3E/7xOavo3/oTLJDUo2WJRPxXExGT2PulxhcyIsSWUKW5vJWxIFWXGhlSwIXjLL6+S5mXFcyve/VWp6mZx5OEETqEMHlxDFe6gDg1gIOEZXuHN0c6L8+58LFpzTjZzDH/gfP4AwCiPlw==</latexit> L L Translation Comprehension Alignment Context in Model Context in Model <latexit sha1_base64="QoFvgP8Odwi21dO3Ce6kmfH0NGc=">AAAB6nicbVA9SwNBEJ2LXzF+RS1tFoNgFe5EiGUgjYVFRPMByRH2NnvJkr29Y3dOCEd+go2FIrb+Ijv/jZvkCk18MPB4b4aZeUEihUHX/XYKG5tb2zvF3dLe/sHhUfn4pG3iVDPeYrGMdTeghkuheAsFSt5NNKdRIHknmDTmfueJayNi9YjThPsRHSkRCkbRSg+Nwd2gXHGr7gJknXg5qUCO5qD81R/GLI24QiapMT3PTdDPqEbBJJ+V+qnhCWUTOuI9SxWNuPGzxakzcmGVIQljbUshWai/JzIaGTONAtsZURybVW8u/uf1Ugxv/EyoJEWu2HJRmEqCMZn/TYZCc4ZyagllWthbCRtTTRnadEo2BG/15XXSvqp6btW7v67U3TyOIpzBOVyCBzWowy00oQUMRvAMr/DmSOfFeXc+lq0FJ585hT9wPn8A3mWNdA==</latexit> CL C<latexit sha1_base64="XPctvbf2pu3H9+8pyJnfFASP+w4=">AAAB6nicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLQjcuK9gFtKJPppB06mYSZG6GEfoIbF4q49Yvc+TdO0yy09cDA4ZxzmXtPkEhh0HW/ndLG5tb2Tnm3srd/cHhUPT7pmDjVjLdZLGPdC6jhUijeRoGS9xLNaRRI3g2mzYXffeLaiFg94izhfkTHSoSCUbTSQ3PYGlZrbt3NQdaJV5AaFLD5r8EoZmnEFTJJjel7boJ+RjUKJvm8MkgNTyib0jHvW6poxI2f5avOyYVVRiSMtX0KSa7+nshoZMwsCmwyojgxq95C/M/rpxje+plQSYpcseVHYSoJxmRxNxkJzRnKmSWUaWF3JWxCNWVo26nYErzVk9dJ56ruuXXv/rrWcIs6ynAG53AJHtxAA+6gBW1gMIZneIU3RzovzrvzsYyWnGLmFP7A+fwB5HWNeA==</latexit> P Target Pivot Language Language 1. Translation into pivot language 2. Extractive RC in pivot language 3. Attention-based answer alignment Figure 1: Overview of our method. αij are the attention weights (attention distribution) in the NMT model. (s; e) and (sL; eL) are the answer spans in the pivot language (e.g. English) and target language L, respectively. that first translates from the target language L to language P with which to train the RC model. the pivot language P , produces an answer in lan- guage P , and back-translates the answer into the 2.1 Translation to Pivot Language language L. Our method achieves this result de- To translate the the context and question from the spite using much smaller translation data than that target language L into the pivot language P , one of a state-of-the-art MT system used in the back- possible approach is to use a web service or a soft- translation baseline. ware package for MT (e.g. Google Translate1) as Analysis of our experiments shows that the a blackbox MT system (Hartrumpf et al., 2009; ability to correctly translate questions is crucial Espla-Gomis` et al., 2012; A.´ Garc´ıa Cumbreras for the end task of RC. In particular, over- et al., 2006). However, this approach does not al- sampling a small set of high quality question low us to access the internal intermediate informa- translations in training the NMT model results tion that is potentially useful for bridging the MT in significant accuracy gains in RC. Moreover, and RC systems. our error analysis shows that under-translation To overcome the limitations, we instead train an and paraphrasing in translation significantly attention-based NMT model (Luong et al., 2015) degrade the downstream RC accuracy, al- as a white-box MT system. Our attention-based though
Load more

Recommended publications
  • 「Citizenship Report 2014
    Citizenship Report 2014 - 2015 Creating a real impact for a better tomorrow, with the power of ICT and the mind of citizenship The future is ahead of us and Japan is moving toward it with strong footsteps. With Tokyo being selected to host the Olympics in 2020, the Japanese society sees the lights breaking through the clouds, starting to illuminate the future. Japan is still facing a number of pressing challenges, such as the trend toward becoming a highly aged society, a shrinking working age population, declining job opportunities, and contracting regional economies. In addressing these social issues, information and communications technology or ICT, has an indispensable role to play, as specifically noted in the Japanese government’s “Declaration to be the World’s Most Advanced IT Nation”. As a leading company of ICT, Microsoft Japan is contributing to finding solutions to these challenges and driving new societal movements especially in the areas of “cloud” and “mobile” technologies. Microsoft’s new mission is to “empower all individuals and organizations on the planet to achieve more.” By connecting ICT with people, ICT with Japan, and ICT with the future, we are determined to serve various communities. We believe this is the natural role of Microsoft Japan—a company that has taken root in Japan and has been growing together with Japanese society. 2 Citizenship Report 2014-2015 Contents 4 Leadership Dialogue What actions are local governments and businesses expected to take to enhance the vibrancy of local communities? —Toward progress in the “regional vitalization” initiative 8 Local Revitalization 10 Work Style New Directions 13 Small Businesses and Startups 14 Education Becoming a “productivity and platform” company 18 Information Security In February 2014, Satya Nadella was appointed as the 20 Contribution to Local Society third CEO of Microsoft Corporation.
    [Show full text]
  • Microsoft 2012 Citizenship Report
    Citizenship at Microsoft Our Company Serving Communities Working Responsibly About this Report Microsoft 2012 Citizenship Report Microsoft 2012 Citizenship Report 01 Contents Citizenship at Microsoft Serving Communities Working Responsibly About this Report 3 Serving communities 14 Creating opportunities for youth 46 Our people 85 Reporting year 4 Working responsibly 15 Empowering youth through 47 Compensation and benefits 85 Scope 4 Citizenship governance education and technology 48 Diversity and inclusion 85 Additional reporting 5 Setting priorities and 16 Inspiring young imaginations 50 Training and development 85 Feedback stakeholder engagement 18 Realizing potential with new skills 51 Health and safety 86 United Nations Global Compact 5 External frameworks 20 Supporting youth-focused 53 Environment 6 FY12 highlights and achievements nonprofits 54 Impact of our operations 23 Empowering nonprofits 58 Technology for the environment 24 Donating software to nonprofits Our Company worldwide 61 Human rights 26 Providing hardware to more people 62 Affirming our commitment 28 Sharing knowledge to build capacity 64 Privacy and data security 8 Our business 28 Solutions in action 65 Online safety 8 Where we are 67 Freedom of expression 8 Engaging our customers 31 Employee giving and partners 32 Helping employees make 69 Responsible sourcing 10 Our products a difference 71 Hardware production 11 Investing in innovation 73 Conflict minerals 36 Humanitarian response 74 Expanding our efforts 37 Providing assistance in times of need 76 Governance 40 Accessibility 77 Corporate governance 41 Empowering people with disabilities 79 Maintaining strong practices and performance 42 Engaging students with special needs 80 Public policy engagement 44 Improving seniors’ well-being 83 Compliance Cover: Participants at the 2012 Imagine Cup, Sydney, Australia.
    [Show full text]
  • Microsoft 2006 Citizenship Report
    partners in innovation 2006 CITIZENSHIP REPO R T about this report ScOPE CUrrENCY In the 2006 Microsoft Global Citizenship Report, All money figures in this report are in we discuss our key citizenship initiatives and U.S. dollars unless otherwise noted. activities worldwide, highlight some of our more significant accomplishments in fiscal year TERMINOLOGY 2006, and share our goals for fiscal year 2007. The terms “Global Citizenship” and “Corporate Citizenship,” which are used throughout REPORTING PERIOD this report, are interchangeable with This report focuses on Microsoft’s fiscal similar terms such as “Corporate Social year 2006 (which began July 1, 2005, Responsibility” and “Corporate Sustainability.” and ended June 30, 2006). All data is for that period unless otherwise noted. GLOBAL REPORTING INITIATIVE (GRI) The function of the GRI performance REPORT STRUCTURE indicators is to make it easier to compare This report is organized to reflect the organizational reports on the basis of economic, structure of our Global Citizenship Initiative environmental, and social impacts. In the and related activities, because we want our online portion of this report, we have included stakeholders — employees, customers, partners, references to applicable GRI indicators to assist shareholders, and others—to be able to stakeholders in their review and assessment compare our program goals and objectives of our Global Citizenship Initiative. with our results. The report is a combination of this print volume, which highlights and More information provides an overview of some key issues, and about the GRI performance indicators is a more detailed online publication that we will available at www.globalreporting.org. update annually.
    [Show full text]
  • Murphy Why Did the Xbox Fail in Japan?
    Travis “L.D.” Murphy Why Did the Xbox Fail in Japan? The sixth console generation was an interesting time in the video game industry. Among other things, it marked the end of the bit wars, the rise of online gaming, and the fall of SEGA from the console market. But in SEGA’s place rose Microsoft with their own Xbox. While the behemoth of a console was unable to shake the PS2’s firm hold on the market, it still managed to edge out Nintendo’s GameCube with over 24 million units sold worldwide. [1] While this victory secured Microsoft’s place as a major player in the console gaming market in the west, the same couldn’t be said for Japan as, despite the company’s efforts, the Xbox barely managed to sell an estimated 450,000 units in the region throughout its lifespan. [2] In fact, on July 18th, 2004 it was reported that even the PlayStation 1, which had been out for almost a decade by that point, outsold the Xbox in Japan too. [3] It’s important to note that Microsoft faced an uphill battle from the very beginning. After all, Japanese companies had a very firm hold on the video game console market ever since the aftermath of the video game crash of 1983. Furthermore, Japan alone represented nearly a third of the market. [4]So, from the early days developing the system, the Xbox team kept Japan close in mind. Then Xbox Director of Third Party Relations, Kevin Bachus, recalled, “We were basically going to play in Sony, Sega and Nintendo's home stadium.
    [Show full text]
  • Security Intelligence Report
    Managing Risks Microsoft | Security Intelligence Report Volume 9 January through June 2010 Microsoft | Security Intelligence Report Microsoft Security Intelligence Report This document is for informational purposes only. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED, OR STATUTORY, AS TO THE INFORMATION IN THIS DOCUMENT. This document is provided “as-is”. Information and views expressed in this document, including URL and other Internet website references, may change without notice. You bear the risk of using it. Copyright © 2010 Microsoft Corporation. All rights reserved. The names of actual companies and products mentioned herein may be the trademarks of their respective owners. 2 January through June 2010 Authors David Anselmi Jimmy Kuo Navaneethan Santhanam Digital Crimes Unit Microsoft Malware Protection Center Bing Richard Boscovich Scott Molenkamp Christian Seifert Digital Crimes Unit Microsoft Malware Protection Center Bing T.J. Campana Michelle Meyer Frank Simorjay Digital Crimes Unit Microsoft Trustworthy Computing Microsoft Trustworthy Computing Neil Carpenter Bala Neerumalla Holly Stewart CSS Security Microsoft Secure SQL Initiative Team Microsoft Malware Protection Center Greg Cottingham Daryl Pecelj Adrian Stone CSS Security Microsoft IT Information Security and Risk Management Microsoft Security Response Center Joe Faulhaber Anthony Penta Matt Thomlinson Microsoft Malware Protection Center Microsoft Windows Safety Platform Microsoft Security Response Center Vinny Gullotto Paul Pottorff Jossie Tirado Arroyo Microsoft Malware
    [Show full text]
  • Microsoft | Security Intelligence Report
    Battling Botnets for Control of Computers Microsoft | Security Intelligence Report Volume 9 January through June 2010 Microsoft | Security Intelligence Report Microsoft Security Intelligence Report This document is for informational purposes only. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED, OR STATUTORY, AS TO THE INFORMA- TION IN THIS DOCUMENT. This document is provided “as-is.” Information and views expressed in this document, including URL and other Internet Web site references, may change without notice. You bear the risk of using it. Copyright © 2010 Microsoft Corporation. All rights reserved. The names of actual companies and products mentioned herein may be the trademarks of their respective owners. 2 January through June 2010 Authors David Anselmi Jimmy Kuo Navaneethan Santhanam Digital Crimes Unit Microsoft Malware Protection Center Bing Richard Boscovich Scott Molenkamp Christian Seifert Digital Crimes Unit Microsoft Malware Protection Center Bing T.J. Campana Michelle Meyer Frank Simorjay Digital Crimes Unit Microsoft Trustworthy Computing Microsoft Trustworthy Computing Neil Carpenter Bala Neerumalla Holly Stewart CSS Security Microsoft Secure SQL Initiative Team Microsoft Malware Protection Center Greg Cottingham Daryl Pecelj Adrian Stone CSS Security Microsoft IT Information Security and Risk Management Microsoft Security Response Center Joe Faulhaber Anthony Penta Matt Thomlinson Microsoft Malware Protection Center Microsoft Windows Safety Platform Microsoft Security Response Center Vinny Gullotto Paul Pottorff Jossie
    [Show full text]
  • A Rewards System to Distinguish Trustworthy Articles
    A Rewards System to Distinguish Trustworthy Articles ALIS is Japan's First Social Media Platform Using Blockchain Technology Masahiro Yasu, Sota Ishii, Takashi Mizusawa Version 1.06: September 6, 2017 © ALIS Co., Ltd. All rights reserved. Contents 1. What is ALIS 2 2. Target market size and the appeal of the Japanese market 4 3. Platform growth strategy in Japan 5 4. Platform introduction and its features 8 5. Screen image introduction 9 6. Why it attracts high quality articles 10 7. Why it can pass on value to people 11 8. Why ALIS can continue to grow for the long term 12 9. How token is created and distributed 12 10. About the detail of distribution logic and adjustment of parameters 13 11. How fraud is prevented 15 12. The blockchain technology used in ALIS 16 13. The vision and mission of the team and profiles of team members 17 14. Financing 19 15. How the money will be used 21 16. The company operation policy 22 17. The details of ICO 23 18. Conclusion 24 1 1. What is ALIS ALIS is Japan's first non-ad funded social media platform. It is a new and novel platform where many individuals can create and find useful articles. With ALIS tokens as the key driver, we remove ads and stealth marketing so that people can quickly access and find high quality articles and trusted individuals. To be honest, we were intrigued by STEEM (https://steem.io), which helped to inspire the conception of ALIS. This article explains the STEEM concept: https://cointelegraph.com/news/steemit-new-social-media-platform-which-pays-you-to-post Our story began with Sota Ishii, our project member who posted several travel photos (https://steemit.com/japanese/@sot528/1-south-america-tour-no-1) on STEEM.
    [Show full text]
  • Four-Day Workweek: the Microsoft Japan Experience
    VC20032 Four-Day Workweek: The Microsoft Japan Experience Courtney Gatlin-Keener, MBA University of the Incarnate Word Ryan Lunsford, PhD University of the Incarnate Word ABSTRACT This case study presents an in-depth analysis of the four-day workweek experiment conducted at Microsoft Japan in August 2019 as part of its Work Life Choice Challenge (WLCC) summer activity. It also evaluates various perspectives surrounding the advantages and limitations of the four-day workweek as applied in the context of Microsoft Japan’s operations. Furthermore, the complexities and uniqueness of the four-day workweek are discussed in the light of the theoretical and practical implications from the literature regarding the adjustment of time management policy and introduction of technology-assisted strategies to optimize work hours. The four-day workweek is seen to be of special significance to the Japanese work culture because of the increasing number of karoshi -related deaths (Takami, 2017). Thus, the case study also expounds on the dilemma of the famed Japanese practice of hard work (Ballon, 2018). In addition, an exploration of best practices, a reflection of lessons learned, and the identification of practical recommendations discovered from the study of Microsoft Japan’s four-day workweek experience are summarized. Keywords: four-day workweek, overwork death, karoshi, overwork suicide, karojisatsu, Japanese work culture, Microsoft Japan, Work Life Choice Challenge, WLCC Four-day workweek, Page 1 VC20032 INTRODUCTION The twenty-first century continues to be a defining period of change and disruption that continues to result in a more virtually and seemingly smaller world (Gilligan, Leontaris, & Lopez, 2019). Along with the influx of innovative technologies, globalization is revolutionizing every social, business, and household process via the pathways of digital connectivity and the imperatives of trade and commerce (Ghemawat, 2018).
    [Show full text]
  • Nintendo Co., Ltd
    Nintendo Co., Ltd. Earnings Release for the Nine-Month Period Ended December 2019 Supplementary Information [Note] Forecasts announced by Nintendo Co., Ltd. herein are prepared based on management's assumptions with information available at this time and therefore involve known and unknown risks and uncertainties. Please note such risks and uncertainties may cause the actual results to be materially different from the forecasts (earnings forecast, dividend forecast and other forecasts). Nintendo Co., Ltd. Consolidated Statements of Income Transition million yen FY3/2016 FY3/2017 FY3/2018 FY3/2019 FY3/2020 Apr.-Dec.'15 Apr.-Dec.'16 Apr.-Dec.'17 Apr.-Dec.'18 Apr.-Dec.'19 Net sales 425,664 311,121 857,012 997,295 1,022,668 Cost of sales 242,364 171,055 530,707 588,222 556,578 Gross profit 183,299 140,065 326,305 409,073 466,089 (Gross profit ratio) (43.1%) (45.0%) (38.1%) (41.0%) (45.6%) Selling, general and 140,814 113,750 169,842 189,043 203,159 administrative expenses Operating profit 42,485 26,315 156,462 220,029 262,930 (Operating profit ratio) (10.0%) (8.5%) (18.3%) (22.1%) (25.7%) Non-operating income 12,993 23,837 38,874 20,469 19,826 (of which foreign exchange gains) (1,801) (1,405) (20,264) (4,349) ( - ) Non-operating expenses 183 1,043 772 230 8,915 (of which foreign exchange losses) ( - ) ( - ) ( - ) ( - ) (8,099) Ordinary profit 55,295 49,110 194,563 240,268 273,841 (Ordinary profit ratio) (13.0%) (15.8%) (22.7%) (24.1%) (26.8%) Extraordinary income 404 63,756 3,240 1 988 Extraordinary losses 940 193 1,428 703 103 Profit before income taxes 54,759 112,673 196,375 239,566 274,725 Income taxes 14,196 9,693 59,253 70,384 78,308 Profit 40,563 102,979 137,122 169,182 196,417 Profit attributable to 4 10 1,957 396 27 non-controlling interests Profit attributable to 40,558 102,969 135,165 168,785 196,389 owners of parent (Profit attributable to (9.5%) (33.1%) (15.8%) (16.9%) (19.2%) owners of parent ratio) -1- Nintendo Co., Ltd.
    [Show full text]
  • The Video Games Cluster in Japan
    Microeconomics of Competitiveness Professor: Michael E. Porter Advisor: Niels Ketelhöhn The Video Games Cluster in Japan Tetsuya Azuma (HKS) 2009/05/08 Marton Sziraczki (HBS) Nobutaka Takeo (HKS) Satoshi Yamada (MIT Sloan) Table of Contents 1. Country Analysis ...........................................................................................................1 1.1 Brief overview of Japan ............................................................................................1 1.2 The Diamond Analysis of Japan .............................................................................7 2. Cluster Analysis ..............................................................................................................13 2.1 Brief overview ..............................................................................................................13 2.2 The Diamond Analysis ................................................................................................16 3. Recommendations and threats ....................................................................................25 4. Bibliography .....................................................................................................................30 1 1. Country Analysis 1.1 Brief overview of Japan Overall picture of Japan : Fig. 1 Overall picture of Japan Japan has a population of 128 million (10th in the world) with among the highest level of literacy in the developed world (Figure 1), dating back to the “Terakoya-system1” in the Edo-Era, Japan had an excellent
    [Show full text]
  • Table of Contents
    Table of Contents President’s Message .......................................................................................................................................5 JCIE Activities: April 2006–March 2008 .....................................................................................................8 Global ThinkNet Policy Research and Dialogue ................................................................................................................. 12 Asia Pacific Agenda Project The Development of Trilateral Cooperation among East Asia, North America, and Europe in Global Governance East Asia and a Rising India: Prospects for the Region 12th APAP Forum, Bali 13th APAP Forum, Singapore Dialogue and Research Monitor: Toward Community Building in East Asia ASEM’s Role in Enhancing Asia-Europe Cooperation: Ten Years of Achievements and Future Challenges East Asia Insights: Toward Community Building An Enhanced Agenda for US-Japan Partnership Human Security Approaches to HIV/AIDS in Asia and Africa Managing China-Japan-US Relations and Strengthening Trilateral Cooperation Survey of the State of US-China Policy-Oriented Intellectual Exchange and Dialogue Survey of US Congressional Approaches to East Asia After the Midterm Elections Preliminary Study on Community Perspectives on Human Security Survey of Trends in US-Japan Exchange Support and Cooperation for Research and Dialogue .......................................................................20 Trilateral Commission UK-Japan 21st Century Group Japanese-German Forum
    [Show full text]
  • Proposal and Evaluation of Kinect-Based Physical Training System for Special Needs Education
    ACHI 2018 : The Eleventh International Conference on Advances in Computer-Human Interactions Proposal and Evaluation of Kinect-based Physical Training System for Special Needs Education Hiroki Matsuda, Hidemi Yamachi, Fumihiro Kumeno Kotomi Shiota Department of Computer and Information Technology Organization for University Research Initiatives Nippon Institute of Technology Waseda Universityline Japan, Miyashiro Japan, Totsukamachi email: [email protected], [email protected], email: [email protected] [email protected] Abstract- Microsoft Kinect is a motion sensing device for of disability inventory scores before and after training [5] and video games with potential applications in rehabilitation or found significantly improved results. physical training systems. Many studies have shown the effectiveness of Kinect-based physical training systems, Few studies have specifically studied the use of such tools particularly for promoting concentration and movement in in the special needs education fields. Chang et al. compared the special needs education. However, the availability of such systems effectiveness of the Kinect system and the high fidelity in the special needs education field in Japan is currently very OptiTrack optical system [6] and showed that Kinect was limited. We developed a Kinect-based physical training system effective enough as a rehabilitation tool for use in both clinical for special needs education and provided it to schools for and home environments. Altanis et al. also found that a Kinect evaluation. To clearly estimate the system efficacy, we developed learning game was effective for children with gross motor skill a motion analyzer utilizing the open-source software OpenPose. problems and motor impairments [7]. Boutiska et al.
    [Show full text]