ABSTRACT
Machine translation (MT) is now widely and freely available, and has the potential to greatly improve cross-lingual communication. In order to use MT reliably and safely, end users must be able to assess the quality of system outputs and determine how much they can rely on them to guide their decisions and actions. However, it can be difficult for users to detect and recover from mistranslations due to limited language skills. In this work we collected 19 MT-mediated role-play conversations in housing and employment scenarios, and conducted in-depth interviews to understand how users identify and recover from translation errors. Participants communicated using four language pairs: English, and one of Spanish, Farsi, Igbo, or Tagalog. We conducted qualitative analysis to understand user challenges in light of limited system transparency, strategies for recovery, and the kinds of translation errors that proved more or less difficult for users to overcome. We found that users broadly lacked relevant and helpful information to guide their assessments of translation quality. Instances where a user erroneously thought they had understood a translation correctly were rare but held the potential for serious consequences in the real world. Finally, inaccurate and disfluent translations had social consequences for participants, because it was difficult to discern when a disfluent message was reflective of the other person’s intentions, or an artifact of imperfect MT. We draw on theories of grounding and repair in communication to contextualize these findings, and propose design implications for explainable AI (XAI) researchers, MT researchers, as well as collaboration among them to support transparency and explainability in MT. These directions include handling typos and non-standard grammar common in interpersonal communication, making MT in interfaces more visible to help users evaluate errors, supporting collaborative repair of conversation breakdowns, and communicating model strengths and weaknesses to users.
- Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N. Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil, and Eric Horvitz. 2019. Guidelines for Human-AI Interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3290605.3300233Google ScholarDigital Library
- Jeanne Batalova and Jie Zong. 2016. Language Diversity and English Proficiency in the United States. Migration Policy Institute. Retrieved July 19, 2021 from https://www.migrationpolicy.org/article/language-diversity-and-english-proficiency-united-states-2015Google Scholar
- Nicole Baumgarten and Inke Du Bois. 2019. Linguistic discrimination and cultural diversity in social spaces. Journal of Language and Discrimination 3, 2 (2019), 85–91.Google ScholarCross Ref
- Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (Virtual Event, Canada) (FAccT ’21). Association for Computing Machinery, New York, NY, USA, 610–623. https://doi.org/10.1145/3442188.3445922Google ScholarDigital Library
- Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo, and Marcello Federico. 2016. Neural versus Phrase-Based Machine Translation Quality: a Case Study. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Austin, Texas, 257–267. https://doi.org/10.18653/v1/D16-1025Google ScholarCross Ref
- L. Bentivogli, M. Cettolo, M. Federico, and C. Federmann. 2018. Machine Translation Human Evaluation: an investigation of evaluation based on Post-Editing and its relation with Direct Assessment. In Proceedings of International Conference on Spoken Language Translation(IWSLT ’18). 62–69.Google Scholar
- Yotam Berger. 2017. Israel Arrests Palestinian Because Facebook Translated ’Good Morning’ to ’Attack Them’. Haaretz (Oct 2017). https://www.haaretz.com/israel-news/palestinian-arrested-over-mistranslated-good-morning-facebook-post-1.5459427Google Scholar
- Hugh Beyer and Karen Holtzblatt. 1997. Contextual Design: Defining Customer-Centered Systems. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.Google ScholarDigital Library
- John Blatz, Erin Fitzgerald, George Foster, Simona Gandrabur, Cyril Goutte, Alex Kulesza, Alberto Sanchis, and Nicola Ueffing. 2004. Confidence Estimation for Machine Translation. In COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics. Geneva, Switzerland, 315–321. https://www.aclweb.org/anthology/C04-1046Google Scholar
- Holly P. Branigan, Martin John Pickering, Jamie Pearson, and Janet McLean. 2010. Linguistic alignment between people and computers. Journal of Pragmatics 42(2010), 2355–2368.Google ScholarCross Ref
- Susan E. Brennan. 1998. The Grounding Problem in Conversations With and Through Computers. Lawrence Erlbaum, Hillsdale, NJ, 201–225.Google Scholar
- Susan E. Brennan and Justina O. Ohaeri. 1994. Effects of Message Style on Users’ Attributions toward Agents. In Conference Companion on Human Factors in Computing Systems (Boston, Massachusetts, USA) (CHI ’94). Association for Computing Machinery, New York, NY, USA, 281–282. https://doi.org/10.1145/259963.260492Google ScholarDigital Library
- Zana Buçinca, Maja Barbara Malaya, and Krzysztof Z. Gajos. 2021. To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-Assisted Decision-Making. Proc. ACM Hum.-Comput. Interact. 5, CSCW1, Article 188 (apr 2021), 21 pages. https://doi.org/10.1145/3449287Google ScholarDigital Library
- Carrie J Cai, Samantha Winter, David Steiner, Lauren Wilcox, and Michael Terry. 2019. ”Hello AI”: Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making. Proceedings of the ACM on Human-computer Interaction 3, CSCW(2019), 1–24.Google ScholarDigital Library
- Fabio Calefato, Filippo Lanubile, Tayana Conte, and Rafael Prikladnicki. 2016. Assessing the impact of real-time machine translation on multilingual meetings in global software projects. Empirical Software Engineering 21, 3 (June 2016), 1002–1034. https://doi.org/10.1007/s10664-015-9372-xGoogle ScholarDigital Library
- Chris Callison-Burch, Philipp Koehn, Christof Monz, Matt Post, Radu Soricut, and Lucia Specia. 2012. Findings of the 2012 Workshop on Statistical Machine Translation. In Proceedings of the Seventh Workshop on Statistical Machine Translation. Association for Computational Linguistics, Montréal, Canada, 10–51. https://www.aclweb.org/anthology/W12-3102Google ScholarDigital Library
- M. Chalmers, I. MacColl, and M. Bell. 2003. Seamful design: showing the seams in wearable computing. In 2003 IEE Eurowearable. 11–16. https://doi.org/10.1049/ic:20030140Google ScholarCross Ref
- Kathy Charmaz. 2014. Constructing grounded theory: A practical guide through qualitative research (2 ed.). SAGE Publications Ltd, London, United Kingdom.Google Scholar
- H. H. Clark and S. E. Brennan. 1991. Grounding in communication. American Psychological Association, 127–149. https://doi.org/10.1037/10096-006Google ScholarCross Ref
- Herbert H. Clark and Deanna Wilkes-Gibbs. 1986. Referring as a collaborative process. Cognition 22(1986), 1–39. Issue 1. https://doi.org/10.1016/0010-0277(86)90010-7Google ScholarCross Ref
- Upol Ehsan, Q. Vera Liao, Michael Muller, Mark O. Riedl, and Justin D. Weisz. 2021. Expanding Explainability: Towards Social Transparency in AI Systems. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, Article 82, 19 pages. https://doi.org/10.1145/3411764.3445188Google ScholarDigital Library
- Upol Ehsan and Mark O. Riedl. 2020. Human-Centered Explainable AI: Towards a Reflective Sociotechnical Approach. In HCI International 2020 - Late Breaking Papers: Multimodality and Intelligence, Constantine Stephanidis, Masaaki Kurosu, Helmut Degen, and Lauren Reinerman-Jones (Eds.). Springer International Publishing, Cham, 449–466.Google Scholar
- Ge Gao, Hao-Chuan Wang, Dan Cosley, and Susan R. Fussell. 2013. Same translation but different experience: the effects of highlighting on machine-translated conversations. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’13). Association for Computing Machinery, Paris, France, 449–458. https://doi.org/10.1145/2470654.2470719Google ScholarDigital Library
- Ge Gao, Bin Xu, Dan Cosley, and Susan R. Fussell. 2014. How Beliefs about the Presence of Machine Translation Impact Multilingual Collaborations. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing (Baltimore, Maryland, USA) (CSCW ’14). Association for Computing Machinery, New York, NY, USA, 1549–1560. https://doi.org/10.1145/2531602.2531702Google ScholarDigital Library
- Edward Golding, Laurie Goodman, and Sarah Strochak. 2018. Is Limited English Proficiency a Barrier to Homeownership?Urban Institute. Retrieved July 19, 2021 from https://www.urban.org/sites/default/files/publication/97436/is_limited_english_proficiency_a_barrier_to_homeownership.pdfGoogle Scholar
- Spence Green, Jason Chuang, Jeffrey Heer, and Christopher D. Manning. 2014. Predictive translation memory: a mixed-initiative system for human language translation. In Proceedings of the 27th annual ACM symposium on User interface software and technology - UIST ’14. ACM Press, Honolulu, Hawaii, USA, 177–187. https://doi.org/10.1145/2642918.2647408Google ScholarDigital Library
- Craig Hadley and Crystal Patil. 2009. Perceived discrimination among three groups of refugees resettled in the USA: associations with language, time in the USA, and continent of origin. Journal of Immigrant and Minority Health 11, 6 (2009), 505–512.Google ScholarCross Ref
- Jeffrey T Hancock, Mor Naaman, and Karen Levy. 2020. AI-Mediated Communication: Definition, Research Agenda, and Ethical Considerations. Journal of Computer-Mediated Communication 25, 1 (Jan. 2020), 89–100. https://doi.org/10.1093/jcmc/zmz022 _eprint: https://academic.oup.com/jcmc/article-pdf/25/1/89/32961176/zmz022.pdf.Google ScholarCross Ref
- Kotaro Hara and Shamsi T. Iqbal. 2015. Effect of Machine Translation in Interlingual Conversation: Lessons from a Formative Study. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems(CHI ’15). Association for Computing Machinery, Seoul, Republic of Korea, 3473–3482. https://doi.org/10.1145/2702123.2702407Google ScholarDigital Library
- Chang Hu, Benjamin B. Bederson, Philip Resnik, and Yakov Kronrod. 2011. MonoTrans2: a new human computation system to support monolingual translation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’11). Association for Computing Machinery, Vancouver, BC, Canada, 1133–1136. https://doi.org/10.1145/1978942.1979111Google ScholarDigital Library
- Sarah Inman and David Ribes. 2019. ”Beautiful Seams”: Strategic Revelations and Concealments. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3290605.3300508Google ScholarDigital Library
- Pierre Isabelle, Colin Cherry, and George Foster. 2017. A Challenge Set Approach to Evaluating Machine Translation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Copenhagen, Denmark, 2486–2496. https://doi.org/10.18653/v1/D17-1263Google ScholarCross Ref
- Alon Jacovi, Ana Marasović, Tim Miller, and Yoav Goldberg. 2021. Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in ai. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 624–635.Google ScholarDigital Library
- Pratik Joshi, Sebastin Santy, Amar Budhiraja, Kalika Bali, and Monojit Choudhury. 2020. The State and Fate of Linguistic Diversity and Inclusion in the NLP World. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 6282–6293. https://doi.org/10.18653/v1/2020.acl-main.560Google ScholarCross Ref
- René F Kizilcec. 2016. How much information? Effects of transparency on trust in an algorithmic interface. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 2390–2395.Google ScholarDigital Library
- Jonathan Lazar, Jinjuan Heidi Feng, and Harry Hochheiser. 2017. Chapter 9 - Ethnography. In Research Methods in Human Computer Interaction (Second Edition) (second edition ed.), Jonathan Lazar, Jinjuan Heidi Feng, and Harry Hochheiser (Eds.). Morgan Kaufmann, Boston, 229–261. https://doi.org/10.1016/B978-0-12-805390-4.00009-1Google ScholarCross Ref
- Daniel J. Liebling, Michal Lahav, Abigail Evans, Aaron Donsbach, Jess Holbrook, Boris Smus, and Lindsey Boran. 2020. Unmet Needs and Opportunities for Mobile Translation AI. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376261Google ScholarDigital Library
- Zachary Chase Lipton. 2016. The Mythos of Model Interpretability. CoRR abs/1606.03490(2016). arXiv:1606.03490http://arxiv.org/abs/1606.03490Google Scholar
- Niklas Luhmann. 1979. Trust: A mechanism for the reduction of social complexity. Trust and power: Two works by Niklas Luhmann (1979), 1–103.Google Scholar
- Marianna Martindale and Marine Carpuat. 2018. Fluency Over Adequacy: A Pilot Study in Measuring User Trust in Imperfect MT. In Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Papers). Association for Machine Translation in the Americas, Boston, MA, USA, 13–25. https://www.aclweb.org/anthology/W18-1803Google Scholar
- Sharan B Merriam and Associates. 2002. Introduction to qualitative research. In Qualitative research in practice: Examples for discussion and analysis. Jossey-Bass, Hoboken, NJ, USA, 1–17.Google Scholar
- Mai Miyabe and Takashi Yoshino. 2009. Accuracy Evaluation of Sentences Translated to Intermediate Language in Back Translation. In Proceedings of the 3rd International Universal Communication Symposium (Tokyo, Japan) (IUCS ’09). Association for Computing Machinery, New York, NY, USA, 30–35. https://doi.org/10.1145/1667780.1667787Google ScholarDigital Library
- Mai Miyabe and Takashi Yoshino. 2010. Influence of Detecting Inaccurate Messages in Real-Time Remote Text-Based Communication via Machine Translation. In Proceedings of the 3rd International Conference on Intercultural Collaboration (Copenhagen, Denmark) (ICIC ’10). Association for Computing Machinery, New York, NY, USA, 59–68. https://doi.org/10.1145/1841853.1841863Google ScholarDigital Library
- Mai Miyabe and Takashi Yoshino. 2011. Can Indicating Translation Accuracy Encourage People to Rectify Inaccurate Translations?. In Human-Computer Interaction. Interaction Techniques and Environments, Julie A. Jacko (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, 368–377.Google Scholar
- Mai Miyabe, Takashi Yoshino, and Tomohiro Shigenobu. 2008. Effects of Repair Support Agent for Accurate Multilingual Communication. In PRICAI 2008: Trends in Artificial Intelligence, Tu-Bao Hoand Zhi-Hua Zhou (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 1022–1027.Google Scholar
- Wilhelmina Nekoto, Vukosi Marivate, Tshinondiwa Matsila, Timi Fasubaa, Taiwo Fagbohungbe, Solomon Oluwole Akinola, Shamsuddeen Muhammad, Salomon Kabongo Kabenamualu, Salomey Osei, Freshia Sackey, Rubungo Andre Niyongabo, Ricky Macharm, Perez Ogayo, Orevaoghene Ahia, Musie Meressa Berhe, Mofetoluwa Adeyemi, Masabata Mokgesi-Selinga, Lawrence Okegbemi, Laura Martinus, Kolawole Tajudeen, Kevin Degila, Kelechi Ogueji, Kathleen Siminyu, Julia Kreutzer, Jason Webster, Jamiil Toure Ali, Jade Abbott, Iroro Orife, Ignatius Ezeani, Idris Abdulkadir Dangana, Herman Kamper, Hady Elsahar, Goodness Duru, Ghollah Kioko, Murhabazi Espoir, Elan van Biljon, Daniel Whitenack, Christopher Onyefuluchi, Chris Chinenye Emezue, Bonaventure F. P. Dossou, Blessing Sibanda, Blessing Bassey, Ayodele Olabiyi, Arshath Ramkilowan, Alp Öktem, Adewale Akinfaderin, and Abdallah Bashir. 2020. Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, Online, 2144–2160. https://doi.org/10.18653/v1/2020.findings-emnlp.195Google ScholarCross Ref
- Jakob Nielsen. 1994. 10 Usability Heuristics for User Interface Design. Retrieved December 19, 2021 from nngroup.com/articles/ten-usability-heuristics/Google Scholar
- Anita Panayiotou, Kerry Hwang, Sue Williams, Terence W H Chong, Dina LoGiudice, Betty Haralambous, Xiaoping Lin, Emiliano Zucchi, Monita Mascitti-Meuter, Anita M Y Goh, Emily You, and Frances Batchelor. 2020. The perceptions of translation apps for everyday health care in healthcare workers and older people: A multi-method study. Journal of Clinical Nursing 29, 17-18 (Sep 2020), 3516–3526. https://doi.org/10.1111/jocn.15390Google ScholarCross Ref
- Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. ”Why Should I Trust You?”: Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD ’16). Association for Computing Machinery, New York, NY, USA, 1135–1144. https://doi.org/10.1145/2939672.2939778Google ScholarDigital Library
- Emanuel A. Schegloff, Gail Jefferson, and Harvey Sacks. 1977. The Preference for Self-Correction in the Organization of Repair in Conversation. Language 53, 2 (1977), 361–382. http://www.jstor.org/stable/413107Google ScholarCross Ref
- Rico Sennrich. 2017. How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. Association for Computational Linguistics, Valencia, Spain, 376–382. https://aclanthology.org/E17-2060Google ScholarCross Ref
- Chunqi Shi, Donghui Lin, and Toru Ishida. 2013. Agent Metaphor for Machine Translation Mediated Communication. In Proceedings of the 2013 International Conference on Intelligent User Interfaces (Santa Monica, California, USA) (IUI ’13). Association for Computing Machinery, New York, NY, USA, 67–74. https://doi.org/10.1145/2449396.2449407Google ScholarDigital Library
- Tomohiro Shigenobu. 2007. Evaluation and Usability of Back Translation for Intercultural Communication. In Usability and Internationalization. Global and Local User Interfaces, Nuray Aykin (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, 259–265.Google Scholar
- JongHo Shin, Panayiotis G. Georgiou, and Shrikanth Narayanan. 2013. Enabling effective design of multimodal interfaces for speech-to-speech translation system: An empirical study of longitudinal user behaviors over time and user strategies for coping with errors. Computer Speech & Language 27, 2 (2013), 554–571. https://doi.org/10.1016/j.csl.2012.02.001 Special Issue on Speech-speech translation.Google ScholarDigital Library
- Hervé Spechbach, Ismahene Sonia Halimi Mallem, Johanna Gerlach, Nikolaos Tsourakis, and Pierrette Bouillon. 2017. Comparison of the quality of two speech translators in emergency settings : A case study with standardized Arabic speaking patients with abdominal pain. In Proceedings of European Congress of Emergency Medicine(EUSEM 2017). Athens, Greece. https://archive-ouverte.unige.ch/unige:100812Google Scholar
- Lucia Specia, Frédéric Blain, Marina Fomicheva, Erick Fonseca, Vishrav Chaudhary, Francisco Guzmán, and André F. T. Martins. 2020. Findings of the WMT 2020 Shared Task on Quality Estimation. In Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, 743–764. https://www.aclweb.org/anthology/2020.wmt-1.79Google Scholar
- Lucia Specia, Frédéric Blain, Varvara Logacheva, Ramón F. Astudillo, and André F. T. Martins. 2018. Findings of the WMT 2018 Shared Task on Quality Estimation. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers. Association for Computational Linguistics, Belgium, Brussels, 689–709. https://doi.org/10.18653/v1/W18-6451Google ScholarCross Ref
- Steve Stecklow. 2018. Why Facebook is losing the war on hate speech in Myanmar. Reuters (Aug 2018). https://www.reuters.com/investigates/special-report/myanmar-facebook-hate/Google Scholar
- People + AI Research team. 2019. Explainability + Trust. https://pair.withgoogle.com/chapter/explainability-trust/Google Scholar
- Lauren Thornton, Bran Knowles, and Gordon Blair. 2021. Fifty Shades of Grey: In Praise of a Nuanced Approach Towards Trustworthy Design. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 64–76.Google ScholarDigital Library
- Antonio Toral and Víctor M. Sánchez-Cartagena. 2017. A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. Association for Computational Linguistics, Valencia, Spain, 1063–1073. https://aclanthology.org/E17-1100Google ScholarCross Ref
- Yeganeh Torbati. 2019. Google Says Google Translate Can’t Replace Human Translators. Immigration Officials Have Used It to Vet Refugees.Pro Publica (September 2019). https://www.propublica.org/article/google-says-google-translate-cant-replace-human-translators-immigration-officials-have-used-it-to-vet-refugeesGoogle Scholar
- Ehsan Toreini, Mhairi Aitken, Kovila Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, and Aad Van Moorsel. 2020. The relationship between trust in AI and trustworthy machine learning technologies. In Proceedings of the 2020 conference on fairness, accountability, and transparency. 272–283.Google ScholarDigital Library
- Anne M Turner, Yong K Choi, Kristin Dew, Ming-Tse Tsai, Alyssa L Bosold, Shuyang Wu, Donahue Smith, and Hendrika Meischke. 2019. Evaluating the Usefulness of Translation Technologies for Emergency Response Communication: A Scenario-Based Study. JMIR Public Health Surveill 5, 1 (Jan 2019). https://doi.org/10.2196/11171Google ScholarCross Ref
- Justin D. Weisz, Mohit Jain, Narendra Nath Joshi, James Johnson, and Ingrid Lange. 2019. BigBlueBot: Teaching Strategies for Successful Human-Agent Interactions. In Proceedings of the 24th International Conference on Intelligent User Interfaces (Marina del Ray, California) (IUI ’19). Association for Computing Machinery, New York, NY, USA, 448–459. https://doi.org/10.1145/3301275.3302290Google ScholarDigital Library
- Elisabeth Wilson, Alice Hm Chen, Kevin Grumbach, Frances Wang, and Alicia Fernandez. 2005. Effects of limited English proficiency and physician language on health care comprehension. Journal of General Internal Medicine 20, 9 (2005), 800–6. https://doi.org/10.1111/j.1525-1497.2005.0174.xGoogle ScholarCross Ref
- Bin Xu, Ge Gao, Susan R. Fussell, and Dan Cosley. 2014. Improving Machine Translation by Showing Two Outputs. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Toronto, Ontario, Canada) (CHI ’14). Association for Computing Machinery, New York, NY, USA, 3743–3746. https://doi.org/10.1145/2556288.2557171Google ScholarDigital Library
- Naomi Yamashita, Rieko Inaba, Hideaki Kuzuoka, and Toru Ishida. 2009. Difficulties in Establishing Common Ground in Multiparty Groups Using Machine Translation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Boston, MA, USA) (CHI ’09). Association for Computing Machinery, New York, NY, USA, 679–688. https://doi.org/10.1145/1518701.1518807Google ScholarDigital Library
- Naomi Yamashita and Toru Ishida. 2006. Automatic Prediction of Misconceptions in Multilingual Computer-Mediated Communication. In Proceedings of the 11th International Conference on Intelligent User Interfaces (Sydney, Australia) (IUI ’06). Association for Computing Machinery, New York, NY, USA, 62–69. https://doi.org/10.1145/1111449.1111469Google ScholarDigital Library
- 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 ’06. ACM Press, Banff, Alberta, Canada, 515. https://doi.org/10.1145/1180875.1180955Google ScholarDigital Library
- Biao Zhang, Philip Williams, Ivan Titov, and Rico Sennrich. 2020. Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 1628–1639. https://doi.org/10.18653/v1/2020.acl-main.148Google ScholarCross Ref
- Yunfeng Zhang, Q Vera Liao, and Rachel KE Bellamy. 2020. Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 295–305.Google ScholarDigital Library
Index Terms
- Understanding and Being Understood: User Strategies for Identifying and Recovering From Mistranslations in Machine Translation-Mediated Chat
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