research-article

Open access

When Children Chat with Machine Translated Text: Problems, Possibilities, Potential

Authors:

Dev Raj Lamichhane,

Scott MackenzieAuthors Info & Claims

IDC '23: Proceedings of the 22nd Annual ACM Interaction Design and Children Conference

Pages 198 - 209

https://doi.org/10.1145/3585088.3589369

Published: 19 June 2023 Publication History

All formats PDF

Abstract

Two cross-lingual (Nepalese and English) letter exchanges took place between school children from Nepal and England, using Digipal; an Android chatting application. Digipal uses Google Translate to enable children to read and reply in their native language. In two studies we analysed the errors made and the effect of errors on children’s understanding and on the flow of conversation. We found that errors of input negatively affected translation, although this can be reduced through initial grammar cleaning. We highlight features of children’s text that cause errors in translation whilst showing how children worked with and around these errors. Errors sometimes added humour and contributed to continuing the conversations.

References

[1]

Md Faruquzzaman Akan. 2018. Transliteration and translation from Bangla into English: A problem solving approach. British Journal of English Linguistics 6, 6 (2018), 1–21.

[2]

Aria Septi Anggaira. 2017. Linguistic errors on narrative text translation using Google Translate.Pedagogy: Journal of English Language Teaching 5, 1 (2017), 1–14.

[3]

Mark Aronoff. 1994. Spelling as culture. In Writing systems and cognition, C. W. Watt (Ed.). Springer, Berlin, 67–86.

[4]

Carl Bereiter and Marlene Scardamalia. 1983. Does learning to write have to be so difficult? In Learning to Write: First Language / Second Language, Ian Pringle, Janice Yalden, and Avia Freedman (Eds.). Rutledge, New York, 20–33.

[5]

Xiaojun Bi, Shumin Zhai, and Barton A. Smith. 2012. Multilingual touchscreen keyboard design and optimization. Human-Computer Interaction 27, 4 (2012), 352–382.

[6]

James J. Bradac. 1992. Thoughts about floors not eaten, lungs ripped, and breathless dogs: Issues in language and dominance. Annals of the International Communication Association 15, 1 (1992), 457–468.

[7]

William Bright. 1996. The Devanagari script. In The world’s writing systems, Robert T. Daniels and William Bright (Eds.). Oxford University Press, New York, 384–390.

[8]

Martin Carnoy. 1974. Education as cultural imperialism. Longman, New York.

[9]

Yuanyuan Chen, Weilan Wang, Tiejun Wang, and Caike Zhaxi. 2019. Multi-function Tibetan input method on Android. In International Conference on Systems and Informatics – ICSAI 2019. IEEE, New York, 643–648.

[10]

William E. Cooper. 1983. Cognitive aspects of skilled typewriting. Springer, New York.

[11]

Lorenzo Coretti and Daniele Pica. 2018. Facebook’s communication protocols, algorithmic filters, and protest. In Social media materialities and protest: Critical reflections, Thomas Poell Mette Mortensen, Christina Neumayer (Ed.). Rutledge, London.

[12]

Elizabeth O. Crawford and Misty M. Kirby. 2008. Fostering students’ global awareness: Technology applications in social studies teaching and learning. Journal of Curriculum and Instruction 2, 1 (2008), 56–73.

[13]

Diane Dagenais, Kelleen Toohey, Alexa Bennett Fox, and Angelpreet Singh. 2017. Multilingual and multimodal composition at school: ScribJab in action. Language and Education 31, 3 (2017), 263–282.

[14]

Colette Daiute. 1989. Play as thought: Thinking strategies of young writers. Harvard Educational Review 59, 1 (1989), 1–24.

[15]

Allison Druin, Elizabeth Foss, Leshell Hatley, Evan Golub, Mona Leigh Guha, Jerry Fails, and Hilary Hutchinson. 2009. How children search the Internet with keyword interfaces. In Proceedings of the 8th International Conference on Interaction Design and Children – IDC 2009. ACM, New York, 89–96.

Digital Library

[16]

Anne Haas Dyson. 1991. Viewpoints: The word and the world: Reconceptualizing written language development or do rainbows mean a lot to little girls?Research in the Teaching of English 25, 1 (1991), 97–123.

[17]

Ethnologue. 2023. Languages of the world. https://www.ethnologue.com/ [Online; accessed Mar 22, ’23].

[18]

Simeon David Flowers. 2015. Developing intercultural communication in an ELF program through digital pen pal exchange. The Center for ELF Journal 1, 1 (2015), 25–39.

[19]

Adrián Fuentes-Luque. 2017. An approach to analysing the quality of menu translations in southern Spain restaurants. Journal of Multilingual and Multicultural Development 38, 2 (2017), 177–188.

[20]

Spence Green, Jeffrey Heer, and Christopher D. Manning. 2013. The efficacy of human post-editing for language translation. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems – CHI 2013. ACM, New York, 439–448.

Digital Library

[21]

Jerome C. Harste. 1984. Language stories & literacy lessons (ERIC No. ED257113). Heinmann Educational Books, Portsmouth, NH.

[22]

Hossein Hassani. 2017. Kurdish interdialect machine translation. In Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects. Association for Computational Linguistics, Philadelphia, 63–72. https://doi.org/10.18653/v1/W17-1208

[23]

Omar Jabak. 2019. Assessment of Arabic-English translation produced by Google Translate. International Journal of Linguistics, Literature and Translation (IJLLT) 0 (2019), 2617–0299.

[24]

Miguel A. Jiménez-Crespo. 2013. Translation and web localization. Routledge, London.

[25]

Manjiri Joshi, Anirudha N. Joshi, Nagraj Emmadi, and Nirav Malsattar. 2014. Swarachakra keyboard for Indic scripts (tutorial). In Proceedings of the 1st International Conference on Mobile Software Engineering and Systems. IEEE, New York, 5–6.

Digital Library

[26]

Akiyo Kano and Janet C. Read. 2009. Causes of simultaneous keystrokes in children and adults. In Proceedings of the IFIP Conference on Human-Computer Interaction – INTERACT 2009. Springer, Berlin, 137–140. https://doi.org/10.1007/978-3-642-03655-2_16

Digital Library

[27]

Akiyo Kano and Janet C. Read. 2009. Text input error categorisation: solving character level insertion ambiguities using Zero Time analysis. In Proceedings of the 23rd British HCI Group Annual Conference on People and Computers: Celebrating People and Technology. British Computer Society, London, 293–302.

[28]

Ravish Kapoor, Angela T. Truong, Catherine N. Vu, and Dam-Thuy Truong. 2020. Successful verbal communication using Google Translate to facilitate awake intubation of a patient with a language barrier: A case report. A&A Practice 14, 4 (2020), 106–108.

[29]

J. Peter Kincaid, Robert P. Fishburne, R. L. Rogers, and Brad S. Chissom. 1975. Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel (no. RBR-8-75). Technical Report. Naval Technical Training Command Millington TN Research Branch, Millington, Tenn.

[30]

Janette K. Klingner. 2004. Assessing reading comprehension. Assessment for Effective Intervention 29, 4 (2004), 59–70. https://doi.org/10.1177/073724770402900408

[31]

Katrin Kohl and Wen-chin Ouyang. 2020. Introducing creative multilingualism. In Creative Multilingualism: A Manifesto, Katrin Kohl, Rajinder Dudrah, Andrew Gosler, Suzanne Graham, Martin Maiden, Wen chin Ouyang, and Matthew Reynolds (Eds.). Open Book Publishers, Cambridge, UK, 1–24.

[32]

I. Scott MacKenzie and R. William Soukoreff. 2002. Text entry for mobile computing: Models and methods, theory and practice. Human-Computer Interaction 17, 2 (2002), 147–198.

[33]

I. Scott MacKenzie and Shawn X. Zhang. 2001. An empirical investigation of the novice experience with soft keyboards. Behaviour and Information Technology 20 (2001), 411–418.

[34]

Aleksandra Małecka and Piotr Marecki. 2018. Literary experiments with automatic translation: A case study of a creative experiment involving King Ubu and Google Translate. In On the Fringes of Literature and Digital Media Culture, Irena Barbara Kalla, Patrycja Poniatowska, and Dorota Michułka (Eds.). Brill Rodopi, Leiden, The Netherlands, 77–88.

[35]

Catherine McBride-Chang, Dan Lin, Phil D. Liu, Dorit Aram, Iris Levin, Jeung-Ryeul Cho, Hua Shu, and Yuping Zhang. 2012. The ABC’s of Chinese: Maternal mediation of Pinyin for Chinese children’s early literacy skills. Reading and Writing 25, 1 (2012), 283–300.

[36]

Amanda C. Miller and Janice M. Keenan. 2009. How word decoding skill impacts text memory: The centrality deficit and how domain knowledge can compensate. Annals of Dyslexia 59, 2 (2009), 99–113.

[37]

Melita Nadhianti. 2016. An analysis of accuracy level of Google Translate in English-Bahasa Indonesia and Bahasa Indonesia-English translations. Journal Article. Yogyakarta: Universitas Negeri Yogyakarta.

[38]

Sarah A. Nagro. 2015. PROSE checklist: Strategies for improving school-to-home written communication. Teaching Exceptional Children 47, 5 (2015), 256–263.

[39]

Peter Newmark. 1988. A textbook of translation. Prentice Hall, New York.

[40]

Pawan Kumar Ojha, Abid Ismail, and Kuppusamy Kundumani Srinivasan. 2021. Perusal of readability with focus on web content understandability. Journal of King Saud University - Computer and Information Sciences 33, 1 (2021), 1–10. https://doi.org/10.1016/j.jksuci.2018.03.007

[41]

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Philadelphia, 311–318. https://doi.org/10.3115/1073083.1073135

Digital Library

[42]

Beryl Plimmer, Liang He, Tariq Zaman, Kasun Karunanayaka, Alvin W Yeo, Garen Jengan, Rachel Blagojevic, and Ellen Yi-Luen Do. 2015. New interaction tools for preserving an old language. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems – CHI 2015. ACM, New York, 3493–3502.

Digital Library

[43]

Janet C. Read. 2002. Optimising text input for young children using computers for creative writing tasks. In Proceedings of 16th British HCI Conference, Vol. 2. British HCI Group, London, 3 pages.

[44]

Janet C. Read, Stuart MacFarlane, and Chris Casey. 2001. Measuring the usability of text input methods for children. In People and Computers XV—Interaction without Frontiers. Springer, London, 559–572.

[45]

Janet C. Read, Stuart MacFarlane, and Chris Casey. 2002. Oops! Silly me! Errors in a handwriting recognition-based text entry interface for children. In Proceedings of the Second Nordic Conference on Human-Computer Interaction – NordiCHI 2002. ACM, New York, 35–40.

Digital Library

[46]

Janet C. Read, Stuart MacFarlane, and Chris Casey. 2003. Good enough for what?: Acceptance of handwriting recognition errors by child users. In Proceedings of the 2003 Conference on Interaction Design and Children – IDC 2003. ACM, New York, 155–155.

Digital Library

[47]

Deborah K. Reed and Sharon Vaughn. 2012. Retell as an indicator of reading comprehension. Scientific Studies of Reading 16, 3 (2012), 187–217. https://doi.org/10.1080/10888438.2010.538780

[48]

Matthew Reynolds, Sowon S. Park, and Kate Clanchy. 2020. Prismatic translation. In Creative Multilingualism: A Manifesto, Katrin Kohl, Rajinder Dudrah, Andrew Gosler, Suzanne Graham, Martin Maiden, Wen chin Ouyang, and Matthew Reynolds (Eds.). Open Book Publishers, Cambridge, UK, 131–150.

[49]

Greg Roberts, Roland Good, and Stephanie Corcoran. 2005. Story retell: A fluency-based indicator of reading comprehension.School Psychology Quarterly 20, 3 (2005), 304.

[50]

Mónica Rodríguez-Castro, Spencer Salas, and Tracey Benson. 2018. To Google Translate™ or not? Newcomer Latino communities in the middle. Middle School Journal 49, 2 (2018), 3–9.

[51]

Alessandra Rossetti. 2019. Evaluating the impact of semi-automation on the readability and comprehensibility of health content. In Translation in Cascading Crises, Federico M. Federici and Sharon O’Brien (Eds.). Routledge, London, 22 pages.

[52]

R. William Soukoreff and I. Scott MacKenzie. 2001. Measuring errors in text entry tasks: An application of the Levenshtein string distance statistic. In Extended Abstracts of the ACM SIGCHI Conference on Human Factors in Computing Systems - CHI 2001. ACM, New York, 319–320. https://doi.org/10.1145/634067.634256

Digital Library

[53]

Paul Stapleton and Becky Leung Ka Kin. 2019. Assessing the accuracy and teachers’ impressions of Google Translate: A study of primary L2 writers in Hong Kong. English for Specific Purposes 56 (2019), 18–34.

[54]

Zinar Susan. 1990. Fifth-graders’ recall of propositional content and causal relationships from expository prose. Journal of Reading Behavior 22, 2 (1990), 181–199.

[55]

Kumiko Tanaka-Ishii and Renu Gupta. 2010. Writing system variation and text entry. In Text Entry Systems: Mobility, Accessibility, Universality, I. Scott MacKenzie and Kumiko Tanaka-Ishii (Eds.). Morgan Kaufmann, San Francisco, 191–202.

[56]

Wilson L. Taylor. 1953. “Cloze Procedure”: A new tool for measuring readability. Journalism Quarterly 30, 4 (1953), 415–433. https://doi.org/10.1177/107769905303000401

[57]

Paul van den Broek, Yuhtsuen Tzeng, Kirsten Risden, Tom Trabasso, and Patricia Basche. 2001. Inferential questioning: Effects on comprehension of narrative texts as a function of grade and timing. Journal of Educational Psychology 93 (09 2001), 521–529. https://doi.org/10.1037//0022-0663.93.3.521

[58]

Naomi Yamashita and Toru Ishida. 2006. Effects of machine translation on collaborative work. In Proceedings of the 2006 20th Anniversary Conference on Computer Supported Cooperative Work – CSCW 2006. ACM, New York, 515–524.

Digital Library

[59]

Shlomit Yanisky-Ravid and Cynthia Martens. 2019. From the myth of Babel to Google Translate: Confronting malicious use of artificial intelligence: Copyright and algorithmic biases in online translations systems. Seattle UL Rev. 43 (2019), 99.

[60]

Richard Zens, Franz Josef Och, and Hermann Ney. 2002. Phrase-based statistical machine translation. In Annual Conference on Artificial Intelligence (LNCS 2479). Springer, Berlin, 18–32.

Cited By

Varatharaj ARead JHorton MLochrie MBrehm L(2024)Children As Evaluators: Understanding Emotion Language AcquisitionProceedings of the 23rd Annual ACM Interaction Design and Children Conference10.1145/3628516.3659413(892-896)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3628516.3659413
Morales-Navarro LKafai YKonda VMetaxa D(2024)Youth as Peer Auditors: Engaging Teenagers with Algorithm Auditing of Machine Learning ApplicationsProceedings of the 23rd Annual ACM Interaction Design and Children Conference10.1145/3628516.3655752(560-573)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3628516.3655752

Index Terms

When Children Chat with Machine Translated Text: Problems, Possibilities, Potential
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
    2. HCI design and evaluation methods
      1. Usability testing
      2. User studies

Recommendations

Oops! silly me! errors in a handwriting recognition-based text entry interface for children
NordiCHI '02: Proceedings of the second Nordic conference on Human-computer interaction

This paper describes an empirical study in which children aged 7 and 8 used handwriting recognition software and hardware to input their own unconstrained text into the computer. The children were observed using the software, and the behaviour of both ...
Accuracy evaluation of sentences translated to intermediate language in back translation
IUCS '09: Proceedings of the 3rd International Universal Communication Symposium

The back-translation method is used to check the accuracy of a sentence translated to a native language. We believe that there exits a positive correlation between the accuracy of sentences translated to an intermediate language and that of back-...
Features of Accuracy Mismatch between Back-Translated Sentences and Target-Translated Sentences
CULTURE-COMPUTING '11: Proceedings of the 2011 Second International Conference on Culture and Computing

In communication using a machine translation, inaccurate translation prevents effective communication between individuals and leads to misunderstandings. Back-translation is used to check the accuracy of a sentence translated to a native language. For ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

IDC '23: Proceedings of the 22nd Annual ACM Interaction Design and Children Conference

June 2023

824 pages

ISBN:9798400701313

DOI:10.1145/3585088

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 June 2023

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

IDC '23

Sponsor:

SIGCHI

IDC '23: Interaction Design and Children

June 19 - 23, 2023

IL, Chicago, USA

Acceptance Rates

Overall Acceptance Rate 172 of 578 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
338
Total Downloads

Downloads (Last 12 months)254
Downloads (Last 6 weeks)25

Reflects downloads up to 22 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Varatharaj ARead JHorton MLochrie MBrehm L(2024)Children As Evaluators: Understanding Emotion Language AcquisitionProceedings of the 23rd Annual ACM Interaction Design and Children Conference10.1145/3628516.3659413(892-896)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3628516.3659413
Morales-Navarro LKafai YKonda VMetaxa D(2024)Youth as Peer Auditors: Engaging Teenagers with Algorithm Auditing of Machine Learning ApplicationsProceedings of the 23rd Annual ACM Interaction Design and Children Conference10.1145/3628516.3655752(560-573)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3628516.3655752

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents