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Design and Evaluation of an Automatic Text Simplification Prototype with Deaf and Hard-of-hearing Readers

Published: 27 October 2024 Publication History

Abstract

Research has observed benefits from providing lexical and syntactic approaches to Automatic Text Simplification (ATS) to Deaf and Hard-of-hearing (DHH) readers. However, little research has explored DHH readers’ design preferences and interactions with these approaches. This work first explores the design space of ATS systems with DHH readers, identifying potential design configurations for evaluation. Open-ended discussion of participants’ design preferences reveal values informing those preferences, including maintaining reading fluency and efficiency, and control over the tool. Using popular design choices from our formative study, we evaluated a prototype that provides various simplification types to explore DHH readers’ interactions with the system. We observed potential conflicts between participants’ values and design preferences, such as the prototype’s impact on participants’ reading speed and participants’ perceived need to reread simplifications suggested by the tool. However, participants found the tool useful, showing a nuanced preference towards world-level lexical simplifications using pop-ups. Our findings highlight the importance of the tool’s design on users’ reading experiences, and provide implications for the design and evaluation of ATS prototypes with target readers.

References

[1]
Chanchal Agrawal and Roshan L Peiris. 2021. I See What You’re Saying: A Literature Review of Eye Tracking Research in Communication of Deaf or Hard of Hearing Users. In Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility (Virtual Event, USA) (ASSETS ’21). Association for Computing Machinery, New York, NY, USA, Article 41, 13 pages. https://doi.org/10.1145/3441852.3471209
[2]
Sweta Agrawal and Marine Carpuat. 2023. Controlling Pre-trained Language Models for Grade-Specific Text Simplification. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 12807–12819. https://doi.org/10.18653/v1/2023.emnlp-main.790
[3]
J. Albertini and C. Mayer. 2011. Using Miscue Analysis to Assess Comprehension in Deaf College Readers. Journal of Deaf Studies and Deaf Education 16, 1 (2011), 35–46. https://doi.org/10.1093/deafed/enq017
[4]
Oliver Alonzo, Lisa Elliot, Becca Dingman, and Matt Huenerfauth. 2020. Reading Experiences and Interest in Reading-Assistance Tools Among Deaf and Hard-of-Hearing Computing Professionals. In The 22nd International ACM SIGACCESS Conference on Computers and Accessibility (Virtual Event, Greece) (ASSETS ’20). Association for Computing Machinery, New York, NY, USA, 13 pages. https://doi.org/10.1145/3373625.3416992
[5]
Oliver Alonzo, Lisa Elliot, Becca Dingman, Sooyeon Lee, Akhter Al Amin, and Matt Huenerfauth. 2022. Reading-Assistance Tools Among Deaf and Hard-of-Hearing Computing Professionals in the U.S.: Their Reading Experiences, Interests and Perceptions of Social Accessibility. ACM Trans. Access. Comput. 15, 2, Article 16 (may 2022), 31 pages. https://doi.org/10.1145/3520198
[6]
Oliver Alonzo, Sooyeon Lee, Mounica Maddela, Wei Xu, and Matt Huenerfauth. 2022. A Dataset of Word-Complexity Judgements from Deaf and Hard-of-Hearing Adults for Text Simplification. In Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022). Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (Virtual), 119–124. https://aclanthology.org/2022.tsar-1.11
[7]
Oliver Alonzo, Matthew Seita, Abraham Glasser, and Matt Huenerfauth. 2020. Automatic Text Simplification Tools for Deaf and Hard of Hearing Adults: Benefits of Lexical Simplification and Providing Users with Autonomy. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376563
[8]
Oliver Alonzo, Jessica Trussell, Becca Dingman, and Matt Huenerfauth. 2021. Comparison of Methods for Evaluating Complexity of Simplified Texts among Deaf and Hard-of-Hearing Adults at Different Literacy Levels. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, Article 279, 12 pages. https://doi.org/10.1145/3411764.3445038
[9]
Oliver Alonzo, Jessica Trussell, Matthew Watkins, Sooyeon Lee, and Matt Huenerfauth. 2022. Methods for Evaluating the Fluency of Automatically Simplified Texts with Deaf and Hard-of-Hearing Adults at Various Literacy Levels. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 267, 10 pages. https://doi.org/10.1145/3491102.3517566
[10]
Fernando Alva-Manchego, Carolina Scarton, and Lucia Specia. 2020. Data-Driven Sentence Simplification: Survey and Benchmark. Computational Linguistics 46, 1 (03 2020), 135–187. https://doi.org/10.1162/coli_a_00370
[11]
Jean F. Andrews and Jana M. Mason. 1991. Strategy Usage among Deaf and Hearing Readers. Exceptional Children 57, 6 (1991), 536–545. https://doi.org/10.1177/001440299105700607 arXiv:https://doi.org/10.1177/001440299105700607PMID: 2070812.
[12]
Tal August, Lucy Lu Wang, Jonathan Bragg, Marti A. Hearst, Andrew Head, and Kyle Lo. 2023. Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing. ACM Trans. Comput.-Hum. Interact. (apr 2023). https://doi.org/10.1145/3589955 Just Accepted.
[13]
A. Banner and Y. Wang. 2011. An Analysis of the Reading Strategies Used by Adult and Student Deaf Readers. Journal of Deaf Studies and Deaf Education 16, 1 (2011), 2–23. https://doi.org/10.1093/deafed/enq027
[14]
Larwan Berke, Christopher Caulfield, and Matt Huenerfauth. 2017. Deaf and Hard-of-Hearing Perspectives on Imperfect Automatic Speech Recognition for Captioning One-on-One Meetings. In Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility (Baltimore, Maryland, USA) (ASSETS ’17). Association for Computing Machinery, New York, NY, USA, 155–164. https://doi.org/10.1145/3132525.3132541
[15]
Jeffrey P. Bigham, Chandrika Jayant, Hanjie Ji, Greg Little, Andrew Miller, Robert C. Miller, Robin Miller, Aubrey Tatarowicz, Brandyn White, Samual White, and Tom Yeh. 2010. VizWiz: Nearly Real-Time Answers to Visual Questions. In Proceedings of the 23nd Annual ACM Symposium on User Interface Software and Technology (New York, New York, USA) (UIST ’10). Association for Computing Machinery, New York, NY, USA, 333–342. https://doi.org/10.1145/1866029.1866080
[16]
Joachim Bingel, Gustavo Paetzold, and Anders Søgaard. 2018. Lexi: A tool for adaptive, personalized text simplification. In Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, Santa Fe, New Mexico, USA, 245–258. https://www.aclweb.org/anthology/C18-1021
[17]
Jeremy Birnholtz and Steven Ibara. 2012. Tracking Changes in Collaborative Writing: Edits, Visibility and Group Maintenance. In Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work (Seattle, Washington, USA) (CSCW ’12). Association for Computing Machinery, New York, NY, USA, 809–818. https://doi.org/10.1145/2145204.2145325
[18]
Stefan Bott, Horacio Saggion, and David Figueroa. 2012. A Hybrid System for Spanish Text Simplification. In Proceedings of the Third Workshop on Speech and Language Processing for Assistive Technologies (Montreal, Canada) (SLPAT ’12). Association for Computational Linguistics, Stroudsburg, PA, USA, 75–84. http://dl.acm.org/citation.cfm?id=2392855.2392865
[19]
Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3, 2 (2006), 77–101. https://doi.org/10.1191/1478088706qp063oa
[20]
Stuart K. Card, Jock D. Mackinlay, and George G. Robertson. 1990. The Design Space of Input Devices. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Seattle, Washington, USA) (CHI ’90). Association for Computing Machinery, New York, NY, USA, 117–124. https://doi.org/10.1145/97243.97263
[21]
C. Chapdelaine, V. Gouaillier, M. Beaulieu, and L. Gagnon. 2007. Improving video captioning for deaf and hearing-impaired people based on eye movement and attention overload. In Human Vision and Electronic Imaging XII, Bernice E. Rogowitz, Thrasyvoulos N. Pappas, and Scott J. Daly (Eds.). Vol. 6492. International Society for Optics and Photonics, SPIE, 64921K. https://doi.org/10.1117/12.703344
[22]
Jin-Woo Chung, Hye-Jin Min, Joonyeob Kim, and Jong C. Park. 2013. Enhancing Readability of Web Documents by Text Augmentation for Deaf People. In Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics (Madrid, Spain) (WIMS ’13). Association for Computing Machinery, New York, NY, USA, Article 30, 10 pages. https://doi.org/10.1145/2479787.2479808
[23]
Michael Crabb, Rhianne Jones, and Mike Armstrong. 2015. The Development of a Framework for Understanding the UX of Subtitles. In Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility (Lisbon, Portugal) (ASSETS ’15). Association for Computing Machinery, New York, NY, USA, 347–348. https://doi.org/10.1145/2700648.2811372
[24]
Liam Cripwell, Joël Legrand, and Claire Gardent. 2023. Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 12053–12059. https://doi.org/10.18653/v1/2023.emnlp-main.739
[25]
Jan De Belder and Marie-Francine Moens. 2010. Text simplification for children. In Proceedings of the SIGIR workshop on accessible search systems. ACM; New York, 19–26.
[26]
Joshua R De Leeuw. 2015. jsPsych: A JavaScript library for creating behavioral experiments in a Web browser. Behavior research methods 47, 1 (2015), 1–12.
[27]
Ashwin Devaraj, William Sheffield, Byron Wallace, and Junyi Jessy Li. 2022. Evaluating Factuality in Text Simplification. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Dublin, Ireland, 7331–7345. https://doi.org/10.18653/v1/2022.acl-long.506
[28]
Siobhan Devlin and Gary Unthank. 2006. Helping Aphasic People Process Online Information. In Proceedings of the 8th International ACM SIGACCESS Conference on Computers and Accessibility (Portland, Oregon, USA) (Assets ’06). ACM, New York, NY, USA, 225–226. https://doi.org/10.1145/1168987.1169027
[29]
Karen Emmorey and Brittany Lee. 2021. The neurocognitive basis of skilled reading in prelingually and profoundly deaf adults. Language and Linguistics Compass 15, 2 (2021), e12407.
[30]
Ziwei Gu, Ian Arawjo, Kenneth Li, Jonathan K. Kummerfeld, and Elena L. Glassman. 2024. An AI-Resilient Text Rendering Technique for Reading and Skimming Documents. arxiv:2401.10873 [cs.HC]
[31]
David Heineman, Yao Dou, Mounica Maddela, and Wei Xu. 2023. Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 3466–3495. https://doi.org/10.18653/v1/2023.emnlp-main.211
[32]
Teresa Hirzle, Jan Gugenheimer, Florian Geiselhart, Andreas Bulling, and Enrico Rukzio. 2019. A Design Space for Gaze Interaction on Head-Mounted Displays. 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, 1–12. https://doi.org/10.1145/3290605.3300855
[33]
Kentaro Inui, Atsushi Fujita, Tetsuro Takahashi, Ryu Iida, and Tomoya Iwakura. 2003. Text Simplification for Reading Assistance: A Project Note. In Proceedings of the Second International Workshop on Paraphrasing - Volume 16 (Sapporo, Japan) (PARAPHRASE ’03). Association for Computational Linguistics, Stroudsburg, PA, USA, 9–16. https://doi.org/10.3115/1118984.1118986
[34]
Dhruv Jain, Angela Lin, Rose Guttman, Marcus Amalachandran, Aileen Zeng, Leah Findlater, and Jon Froehlich. 2019. Exploring Sound Awareness in the Home for People Who Are Deaf or Hard of Hearing. Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3290605.3300324
[35]
Chao Jiang, Mounica Maddela, Wuwei Lan, Yang Zhong, and Wei Xu. 2020. Neural CRF Model for Sentence Alignment in Text Simplification. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 7943–7960. https://doi.org/10.18653/v1/2020.acl-main.709
[36]
Sushant Kafle, Abraham Glasser, Sedeeq Al-khazraji, Matt Setia, and Matt Huenerfauth. 2019. Artificial Intelligence Fairness in the Context of Accessibility Research on Intelligent Systems for People who are Deaf or Hard of Hearing. In The ACM ASSETS 2019 Workshop on AI Fairness for People with Disabilities. arXiv:1908.10414 [cs.HC]
[37]
Sushant Kafle, Peter Yeung, and Matt Huenerfauth. 2019. Evaluating the Benefit of Highlighting Key Words in Captions for People Who Are Deaf or Hard of Hearing. In Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility (Pittsburgh, PA, USA) (ASSETS ’19). Association for Computing Machinery, New York, NY, USA, 43–55. https://doi.org/10.1145/3308561.3353781
[38]
Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez, Sweta Agrawal, Dennis Aumiller, Fernando Alva-Manchego, and Matthew Shardlow. 2023. BLESS: Benchmarking Large Language Models on Sentence Simplification. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 13291–13309. https://doi.org/10.18653/v1/2023.emnlp-main.821
[39]
Joongwon Kim, Mounica Maddela, Reno Kriz, Wei Xu, and Chris Callison-Burch. 2021. BiSECT: Learning to Split and Rephrase Sentences with Bitexts. arxiv:2109.05006 [cs.CL]
[40]
Yea-Seul Kim, Jessica Hullman, and Eytan Adar. 2015. Descipher: A text simplification tool for science journalism. In Computation+ Journalism Symposium.
[41]
Ilan Kirsh. 2020. Directions and Speeds of Mouse Movements on a Website and Reading Patterns: A Web Usage Mining Case Study. In Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics (Biarritz, France) (WIMS 2020). Association for Computing Machinery, New York, NY, USA, 129–138. https://doi.org/10.1145/3405962.3405982
[42]
Ilan Kirsh. 2020. Using Mouse Movement Heatmaps To Visualize User Attention To Words. In Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society (Tallinn, Estonia) (NordiCHI ’20). Association for Computing Machinery, New York, NY, USA, Article 117, 5 pages. https://doi.org/10.1145/3419249.3421250
[43]
Ilan Kirsh and Mike Joy. 2020. Exploring Pointer Assisted Reading (PAR): Using Mouse Movements to Analyze Web Users’ Reading Behaviors and Patterns. In HCI International 2020 - Late Breaking Papers: Multimodality and Intelligence: 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings (Copenhagen, Denmark). Springer-Verlag, Berlin, Heidelberg, 156–173. https://doi.org/10.1007/978-3-030-60117-1_12
[44]
Poorna Kushalnagar, Scott Smith, Melinda Hopper, Claire Ryan, Micah Rinkevich, and Raja Kushalnagar. 2018. Making cancer health text on the Internet easier to read for deaf people who use American Sign Language. Journal of Cancer Education 33, 1 (2018), 134–140.
[45]
Philippe Laban, Jesse Vig, Wojciech Kryscinski, Shafiq Joty, Caiming Xiong, and Chien-Sheng Wu. 2023. SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Toronto, Canada, 10674–10695. https://doi.org/10.18653/v1/2023.acl-long.596
[46]
Qisheng Li, Meredith Ringel Morris, Adam Fourney, Kevin Larson, and Katherina Reinecke. 2019. The Impact of Web Browser Reader Views on Reading Speed and User Experience. In CHI 2019. ACM. https://www.microsoft.com/en-us/research/publication/the-impact-of-web-browser-reader-views-on-reading-speed-and-user-experience/
[47]
Slavina Lozanova, Ivelina Stoyanova, Svetlozara Leseva, Svetla Koeva, and Boian Savtchev. 2013. Text modification for Bulgarian sign language users. In Proceedings of the Second Workshop on Predicting and Improving Text Readability for Target Reader Populations. 39–48.
[48]
Mounica Maddela, Fernando Alva-Manchego, and Wei Xu. 2021. Controllable Text Simplification with Explicit Paraphrasing. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 3536–3553.
[49]
Mounica Maddela, Yao Dou, David Heineman, and Wei Xu. 2023. LENS: A Learnable Evaluation Metric for Text Simplification. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Toronto, Canada, 16383–16408. https://doi.org/10.18653/v1/2023.acl-long.905
[50]
Mounica Maddela and Wei Xu. 2018. A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, 3749–3760. https://doi.org/10.18653/v1/D18-1410
[51]
Louis Martin, Angela Fan, Éric de la Clergerie, Antoine Bordes, and Benoît Sagot. 2022. MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases. In Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, 1651–1664. https://aclanthology.org/2022.lrec-1.176
[52]
Tara Matthews, Scott Carter, Carol Pai, Janette Fong, and Jennifer Mankoff. 2006. Scribe4Me: Evaluating a Mobile Sound Transcription Tool for the Deaf. In UbiComp 2006: Ubiquitous Computing, Paul Dourish and Adrian Friday (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 159–176.
[53]
Lourdes Moreno, Rodrigo Alarcon, Isabel Segura-Bedmar, and Paloma Martínez. 2019. Lexical Simplification Approach to Support the Accessibility Guidelines. In Proceedings of the XX International Conference on Human Computer Interaction (Donostia, Gipuzkoa, Spain) (Interacción ’19). ACM, New York, NY, USA, Article 14, 4 pages. https://doi.org/10.1145/3335595.3335651
[54]
Hugo Nicolau, André Rodrigues, André Santos, Tiago Guerreiro, Kyle Montague, and João Guerreiro. 2019. The Design Space of Nonvisual Word Completion. In The 21st International ACM SIGACCESS Conference on Computers and Accessibility (Pittsburgh, PA, USA) (ASSETS ’19). Association for Computing Machinery, New York, NY, USA, 249–261. https://doi.org/10.1145/3308561.3353786
[55]
Christina Niklaus, Matthias Cetto, André Freitas, and Siegfried Handschuh. 2019. Transforming Complex Sentences into a Semantic Hierarchy. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 3415–3427. https://doi.org/10.18653/v1/P19-1333
[56]
Constantin Orăsan, Richard Evans, and Ruslan Mitkov. 2018. Intelligent text processing to help readers with autism. Intelligent Natural Language Processing: Trends and Applications (2018), 713–740.
[57]
Carol Padden and Tom Humphries. 2009. Inside Deaf Culture. Harvard University Press. http://www.jstor.org/stable/j.ctvjz83v3
[58]
LeAdelle Phelps and Barbara Jane Branyan. 1990. Academic achievement and nonverbal intelligence in public school hearing-impaired children. Psychology in the Schools 27, 3 (1990), 210–217.
[59]
Piotr Przybyła and Matthew Shardlow. 2020. Multi-Word Lexical Simplification. In Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, Barcelona, Spain (Online), 1435–1446. https://doi.org/10.18653/v1/2020.coling-main.123
[60]
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. J. Mach. Learn. Res. 21, 1, Article 140 (jan 2020), 67 pages.
[61]
Luz Rello, Ricardo Baeza-Yates, Stefan Bott, and Horacio Saggion. 2013. Simplify or Help?: Text Simplification Strategies for People with Dyslexia. In Proceedings of the 10th International Cross-Disciplinary Conference on Web Accessibility (Rio de Janeiro, Brazil) (W4A ’13). ACM, New York, NY, USA, Article 15, 10 pages. https://doi.org/10.1145/2461121.2461126
[62]
Luz Rello, Ricardo Baeza-Yates, Laura Dempere-Marco, and Horacio Saggion. 2013. Frequent words improve readability and short words improve understandability for people with dyslexia. In IFIP Conference on Human-Computer Interaction(INTERACT 2013). Springer, 203–219.
[63]
Luz Rello, Roberto Carlini, Ricardo Baeza-Yates, and Jeffrey P. Bigham. 2015. A Plug-in to Aid Online Reading in Spanish. In Proceedings of the 12th Web for All Conference (Florence, Italy) (W4A ’15). Association for Computing Machinery, New York, NY, USA, Article 7, 4 pages. https://doi.org/10.1145/2745555.2746661
[64]
Naomi B Robbins, Richard M Heiberger, 2011. Plotting Likert and other rating scales. In Proceedings of the 2011 Joint Statistical Meeting. 1058–1066.
[65]
Alessandra Rossetti and Luuk Van Waes. 2022. It’s not just a phase: Investigating text simplification in a second language from a process and product perspective. Frontiers in Artificial Intelligence 5 (2022), 983008.
[66]
Horacio Saggion and Graeme Hirst. 2017. Automatic text simplification. Vol. 32. Springer.
[67]
Horacio Saggion, Sanja Štajner, Stefan Bott, Simon Mille, Luz Rello, and Biljana Drndarevic. 2015. Making It Simplext: Implementation and Evaluation of a Text Simplification System for Spanish. ACM Trans. Access. Comput. 6, 4, Article 14 (May 2015), 36 pages. https://doi.org/10.1145/2738046
[68]
Chantal Shaib, Millicent Li, Sebastian Joseph, Iain Marshall, Junyi Jessy Li, and Byron Wallace. 2023. Summarizing, Simplifying, and Synthesizing Medical Evidence using GPT-3 (with Varying Success). In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Toronto, Canada, 1387–1407. https://doi.org/10.18653/v1/2023.acl-short.119
[69]
Matthew Shardlow. 2014. A survey of automated text simplification. International Journal of Advanced Computer Science and Applications 4, 1 (2014), 58–70.
[70]
Kim Cheng Sheang and Horacio Saggion. 2021. Controllable Sentence Simplification with a Unified Text-to-Text Transfer Transformer. In Proceedings of the 14th International Conference on Natural Language Generation. Association for Computational Linguistics, Aberdeen, Scotland, UK, 341–352. https://aclanthology.org/2021.inlg-1.38
[71]
Svetlana Sheremetyeva. 2014. Automatic text simplification for handling intellectual property (the case of multiple patent claims). In Proceedings of the Workshop on Automatic Text Simplification-Methods and Applications in the Multilingual Society (ATS-MA 2014). 41–52.
[72]
Advaith Siddharthan. 2014. A survey of research on text simplification. ITL - International Journal of Applied Linguistics 165, 2 (2014), 259–298. https://doi.org/10.1075/itl.165.2.06sid
[73]
Sanja Stajner. 2021. Automatic Text Simplification for Social Good: Progress and Challenges. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics, Online, 2637–2652. https://doi.org/10.18653/v1/2021.findings-acl.233
[74]
Chiara Vettori and Ornella Mich. 2011. Supporting Deaf Children’s Reading Skills: The Many Challenges of Text Simplification. In The Proceedings of the 13th International ACM SIGACCESS Conference on Computers and Accessibility (Dundee, Scotland, UK) (ASSETS ’11). Association for Computing Machinery, New York, NY, USA, 283–284. https://doi.org/10.1145/2049536.2049608
[75]
Shaun Wallace, Zoya Bylinskii, Jonathan Dobres, Bernard Kerr, Sam Berlow, Rick Treitman, Nirmal Kumawat, Kathleen Arpin, Dave B. Miller, Jeff Huang, and Ben D. Sawyer. 2022. Towards Individuated Reading Experiences: Different Fonts Increase Reading Speed for Different Individuals. ACM Trans. Comput.-Hum. Interact. 29, 4, Article 38 (mar 2022), 56 pages. https://doi.org/10.1145/3502222
[76]
Dakuo Wang, Judith S. Olson, Jingwen Zhang, Trung Nguyen, and Gary M. Olson. 2015. DocuViz: Visualizing Collaborative Writing. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI ’15). Association for Computing Machinery, New York, NY, USA, 1865–1874. https://doi.org/10.1145/2702123.2702517
[77]
Dakuo Wang, Haodan Tan, and Tun Lu. 2017. Why Users Do Not Want to Write Together When They Are Writing Together: Users’ Rationales for Today’s Collaborative Writing Practices. Proc. ACM Hum.-Comput. Interact. 1, CSCW, Article 107 (dec 2017), 18 pages. https://doi.org/10.1145/3134742
[78]
Willian Massami Watanabe, Arnaldo Candido Junior, Vinícius Rodriguez Uzêda, Renata Pontin de Mattos Fortes, Thiago Alexandre Salgueiro Pardo, and Sandra Maria Aluísio. 2009. Facilita: Reading Assistance for Low-literacy Readers. In Proceedings of the 27th ACM International Conference on Design of Communication (Bloomington, Indiana, USA) (SIGDOC ’09). ACM, New York, NY, USA, 29–36. https://doi.org/10.1145/1621995.1622002
[79]
Gary S. Wilkinson and Gary J. Robertson. 2006. Wide Range Achievement Test 4 professional manual. Psychological Assessment Resources, Inc.
[80]
Menglin Xia, Ekaterina Kochmar, and Ted Briscoe. 2016. Text Readability Assessment for Second Language Learners. In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics, San Diego, CA, 12–22. https://doi.org/10.18653/v1/W16-0502
[81]
Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, and Chris Callison-Burch. 2016. Optimizing Statistical Machine Translation for Text Simplification. Transactions of the Association for Computational Linguistics 4 (2016), 401–415. https://cocoxu.github.io/publications/tacl2016-smt-simplification.pdf
[82]
Momona Yamagami, Sasa Junuzovic, Mar Gonzalez-Franco, Eyal Ofek, Edward Cutrell, John R. Porter, Andrew D. Wilson, and Martez E. Mott. 2022. Two-In-One: A Design Space for Mapping Unimanual Input into Bimanual Interactions in VR for Users with Limited Movement. ACM Trans. Access. Comput. 15, 3, Article 23 (jul 2022), 25 pages. https://doi.org/10.1145/3510463
[83]
Victoria Yaneva, Constantin Orasan, Le An Ha, and Natalia Ponomareva. 2019. A Survey of the Perceived Text Adaptation Needs of Adults with Autism. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019). INCOMA Ltd., Varna, Bulgaria, 1356–1363. https://doi.org/10.26615/978-954-452-056-4_155
[84]
Chris Yimam, Seid Muhie andBiemann. 2018. Par4Sim – Adaptive Paraphrasing for Text Simplification. In Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, Santa Fe, New Mexico, USA, 331–342. https://www.aclweb.org/anthology/C18-1028
[85]
Xingxing Zhang and Mirella Lapata. 2017. Sentence Simplification with Deep Reinforcement Learning. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Copenhagen, Denmark, 584–594. https://doi.org/10.18653/v1/D17-1062

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  1. Design and Evaluation of an Automatic Text Simplification Prototype with Deaf and Hard-of-hearing Readers

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        cover image ACM Conferences
        ASSETS '24: Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility
        October 2024
        1475 pages
        ISBN:9798400706776
        DOI:10.1145/3663548
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        Published: 27 October 2024

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        Author Tags

        1. Accessibility
        2. Automatic Text Simplification
        3. Deaf and Hard-of-hearing Adults
        4. Design Space
        5. Reading

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