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TSAR-2022










Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)

Fully Virtual Workshop(Workshop Program)
Workshop and Shared Task at EMNLP 2022

UPDATES

19th July -- Registration open at the Shared Task on Lexical Simplification for English, Portuguese and Spanish: Shared Task Task website
25th July -- All submissions to the Workshop have to go through softconf. Deadline: 7th September.
4th September -- Deadline extension to 13th September 2022.
7th September -- Deadline for Shared Task Registration
8th September -- Shared Task: Release of Test set release (without gold annotations).
15th September -- Shared Task: Deadline for Submission of systems' outputs
10th November -- Early registration: Early Registration deadline At least one author of each accepted paper must register for EMNLP 2022 by the early registration deadline. . Early Registration: Ends 10 November 2022, 11:59pm EDT.
ANNOUNCEMENT: -- The TSAR-2022 Workshop will be fully VIRTUAL/ONLINE

24th November -- ANNOUNCEMENT: Special Issue in the journal Frontiers in Artificial Intelligence
*** We are glad to announce the launch of a call for contributions for an Special Issue in the journal Frontiers in Artificial Intelligence on the topic of the Workshop and Shared Task: Text Simplification, Accessibility, and Readability. ***
Please check all details at https://www.frontiersin.org/research-topics/47943/text-simplification-accessibility-and-readability


24th November -- ANNOUNCEMENT: TSAR-2022 workshop Sponsorship by Frontiers
*** We are very happy to announce that Frontiers is sponsoring our TSAR 2022 workshop ***







Description

The Text Simplification, Accessibility, and Readability (TSAR) workshop aims at bringing together researchers, developers and industries of assistive technologies, public organizations representatives, and other parties interested in the problem of making information more accessible to all citizens. We will discuss recent trends and developments in the area of automatic text simplification, automatic readability assessment, language resources and evaluation for text simplification, etc.. The workshop will be an online event or hybrid event (depending on the evolution of the COVID pandemic) held during the  EMNLP-2022 conference on 8 of December, 2022.

Call for Papers

Web provides an abundance of knowledge and information that can reach large populations. However, the way in which a text is written (vocabulary, syntax, or text organization/structure), or presented, can make it inaccessible for many people, especially for non-native speakers, people with low literacy, and people with some type of cognitive or linguistic impairments. The results of Adult Literacy Survey (OECD, 2023) indicate that approximately 16.7% of adult population (averaged over 24 highly-developed countries) requires lexical, 50% syntactic, and 89.4% conceptual simplification of everyday texts (Štajner, 2021).

Research on automatic text simplification (TS), textual accessibility, and readability thus have the potential to improve social inclusion of marginalized populations. These related research areas have increasingly attracted more and more attention in the past ten years, evidenced by the growing number of publications in NLP conferences. While only about 300 articles in Google Scholar mentioned TS in 2010, this number has increased to about 600 in 2015 and greater than 1000 in 2020 (Štajner, 2021).

Recent research in automatic text simplification has mostly focused on proposing the use of methods derived from the deep learning paradigm (Glavaš and Štajner, 2015; Paetzold and Specia, 2016; Nisioi et al., 2017; Zhang and Lapata, 2017; Martin et al., 2020; Maddela et al., 2021; Sheang and Saggion, 2021). However, there are many important aspects of the automatic text simplification that need the attention of our community: the design of appropriate evaluation metrics, the development of context-aware simplification solutions, the creation of appropriate language resources to support research and evaluation, the deployment of simplification in real environments for real users, the study of discourse factors in text simplification, the identification of factors affecting the readability of a text, etc. To overcome those issues, there is a need for collaboration of CL/NLP researchers, machine learning and deep learning researchers, UI/UX and Accessibility professionals, as well as public organizations representatives (Štajner, 2021).

The proposed TSAR workshop builds upon the recent success of several regional workshops that covered a subset of our topics of interest, including the SEPLN 2021 Current Trends in Text Simplification (CTTS) and the SimpleText workshop at CLEF 2021, as well as the birds-of-a-feather event on Text Simplification at NAACL 2021 (over 50 participants).

The TSAR workshop aims to foster collaboration among all parties interested in making information more accessible to all people. Through the two invited talks, a shared task on lexical simplification, the round table discussion, regular oral and poster presentations of workshop papers, we will discuss recent trends and developments in the area of automatic text simplification, text accessibility, automatic readability assessment, language resources and evaluation for text simplification, etc.


Topics 


We invite contributions on the following topics (among others):  
 

Submissions

Invited Speakers



Matt Huenerfauth

Rochester Institute of Technology (RIT)

Sowmya Vajjala

National Research Council of Canada


Program

Thursday, December 8, 2022 (GMT+4 - Abu Dhabi time zone)
09:30 - 09:45 Opening Remarks
09:45 - 10:30 Session 1
Parallel Corpus Filtering for Japanese Text Simplification
Koki Hatagaki, Tomoyuki Kajiwara and Takashi Ninomiya
Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset
Jan Trienes, Jörg Schlötterer, Hans-ulrich Schildhaus and Christin Seifert
IrekiaLF_es: a New Open Benchmark and Baseline Systems for Spanish AutomaticText Simplification
Itziar Gonzalez-Dios, Iker GutiérrezFandiño, Oscar M. CumbicusPineda and Aitor Soroa
10:30 - 11:00 Coffee Break
11:00 - 12:30 Session 2
Lexically Constrained Decoding with Edit Operation Prediction for Controllable Text Simplification
Tatsuya Zetsu, Tomoyuki Kajiwara and Yuki Arase
(Psycho-)Linguistic Features Meet Transformer Models for Improved Explainable and Controllable Text Simplification
Yu Qiao, Xiaofei Li, Daniel Wiechmann and Elma Kerz
A Dataset of Word-Complexity Judgements from Deaf and Hard-of-Hearing Adults for Text Simplification
Oliver Alonzo, Sooyeon Lee, Mounica Maddela, Wei Xu and Matt Huenerfauth
Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence models
Patrick Haller, Andreas Säuberli, Sarah Kiener, Jinger Pan, Ming Yan and Len Jäger
Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification
Horacio Saggion, Sanja Štajner, Daniel Ferrés, Kim Cheng Sheang, Matthe Shardlow, Kai North and Marcos Zampieri
UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification?
Dennis Aumiller and Michael Gertz
12:30 - 14:00 Lunch Break
14:00 - 15:30 Session 3 (Posters)
15:30 - 16:00 Coffee Break
16:00 - 16:30 Round Table
16:30 - 17:30 Invited talk 1: Matt Huenerfauth - Abstract and Bio
17:45 - 18:45 Invited talk 2: Sowmya Vajjala - Abstract and Bio
18:45 - 19:00 Closing Statements

Accepted Papers

Workshop Papers
The Fewer Splits are Better: Deconstructing Readability in Sentence Splitting (Poster)
Tadashi Nomoto
Parallel Corpus Filtering for Japanese Text Simplification (Oral)
Koki Hatagaki, Tomoyuki Kajiwara and Takashi Ninomiya
Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset (Oral)
Jan Trienes, Jörg Schlötterer, Hans-Ulrich Schildhaus and Christin Seifert
Target-Level Sentence Simplification as Controlled Paraphrasing (Poster)
Tannon Kew and Sarah Ebling
Conciseness: An Overlooked Language Task (Poster)
Felix Stahlberg, Aashish Kumar, Chris Alberti and Shankar Kumar
Revision for Concision: A Constrained Paraphrase Generation Task (Poster)
Wenchuan Mu and Kwan Hui Lim
Controlling Japanese Machine Translation Output by Using JLPT Vocabulary Levels (Poster)
Alberto Poncelas and Ohnmar Htun
IrekiaLF_es: a New Open Benchmark and Baseline Systems for Spanish Automatic Text Simplification (Oral)
Itziar Gonzalez-Dios, Iker Gutiérrez-Fandiño, Oscar M. Cumbicus-Pineda and Aitor Soroa
Lexical Simplification in Foreign Language Learning: Creating Pedagogically Suitable Simplified Example Sentences (Poster)
Jasper Degraeuwe and Horacio Saggion
Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence models (Oral)
Patrick Haller, Andreas Säuberli, Sarah Kiener, Jinger Pan, Ming Yan and Lena Jäger
A Dataset of Word-Complexity Judgements from Deaf and Hard-of-Hearing Adults for Text Simplification (Oral)
Oliver Alonzo, Sooyeon Lee, Mounica Maddela, Wei Xu and Matt Huenerfauth
(Psycho-)Linguistic Features Meet Transformer Models for Improved Explainable and Controllable Text Simplification (Oral)
Yu Qiao, Xiaofei Li, Daniel Wiechmann and Elma Kerz
Lexically Constrained Decoding with Edit Operation Prediction for Controllable Text Simplification (Oral)
Tatsuya Zetsu, Tomoyuki Kajiwara and Yuki Arase
An Investigation into the Effect of Control Tokens on Text Simplification (Poster)
Zihao Li, Matthew Shardlow and Saeed Hassan
Divide-and-Conquer Text Simplification by Scalable Data Enhancement (Poster)
Sanqiang Zhao, Rui Meng, Hui Su and Daqing He
Improving Text Simplification with Factuality Error Detection (Poster)
Yuan Ma, Sandaru Seneviratne and Elena Daskalaki
JADES: New Text Simplification Dataset in Japanese Targeted at Non-Native Speakers (Poster)
Akio Hayakawa, Tomoyuki Kajiwara, Hiroki Ouchi and Taro Watanabe
A Benchmark for Neural Readability Assessment of Texts in Spanish (Poster)
Laura Vásquez-Rodríguez, Pedro-Manuel Cuenca-Jiménez, Sergio Morales-Esquivel and Fernando Alva-Manchego
Controllable Lexical Simplification for English (Poster)
Kim Cheng Sheang, Daniel Ferrés and Horacio Saggion
Shared Task Papers
Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification (Oral)
Horacio Saggion, Sanja Štajner, Daniel Ferrés, Kim Cheng Sheang, Matthe Shardlow, Kai North and Marcos Zampieri
CILS at TSAR-2022 Shared Task: Investigating the Applicability of Lexical Substitution Methods for Lexical Simplification (Poster)
Sandaru Seneviratne, Elena Daskalaki and Hanna Suominen
PresiUniv at TSAR-2022 Shared Task: Generation and Ranking of Simplification Substitutes of Complex Words in Multiple Languages (Poster)
Peniel Whistely, Sandeep Mathias and Galiveeti Poornima
UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification (Poster)
Laura Vásquez-Rodríguez, Nhung Nguyen, Matthew Shardlow and Sophia Ananiadou
PolyU-CBS at TSAR-2022 Shared Task: A Simple, Rank-Based Method for Complex Word Substitution in Two Steps (Poster)
Emmanuele Chersoni and Yu-Yin Hsu
CENTAL at TSAR-2022 Shared Task: How Does Context Impact BERT-Generated Substitutions for Lexical Simplification? (Poster)
Rodrigo Wilkens, David Alfter, Rémi Cardon, Isabelle Gribomont, Adrien Bibal, Watrin Patrick, Marie-Catherine De marneffe and Thomas François
teamPN at TSAR-2022 Shared Task: Lexical Simplification using Multi-Level and Modular Approach (Poster)
Nikita Nikita and Pawan Rajpoot
MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders (Poster)
Xiaofei Li, Daniel Wiechmann, Yu Qiao and Elma Kerz
UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification? (Oral)
Dennis Aumiller and Michael Gertz
RCML at TSAR-2022 Shared Task: Lexical Simplification With Modular Substitution Candidate Ranking (Poster)
Desislava Aleksandrova and Olivier Brochu Dufour
GMU-WLV at TSAR-2022 Shared Task: Evaluating Lexical Simplification Models (Poster)
Kai North, Alphaeus Dmonte, Tharindu Ranasinghe and Marcos Zampieri

Proceedings


All accepted papers will be included in the workshop proceedings and published in ACL Anthology.
Extended versions of the best papers will be invited for a special issue of Frontiers in Artificial Intelligence focused on: applied research for TS and readability assessment in the context of TS.

Organizers

Sanja Štajner

NLP Researcher, Germany

Horacio Saggion

Chair in Computer Science and Artificial Intelligence and Head of the LaSTUS Lab in the TALN-DTIC, Universitat Pompeu Fabra

Wei Xu

Assistant Professor at School of Interactive Computing, Georgia Institute of Technology

Marcos Zampieri

Assistant Professor at the Rochester Institute of Technology

Matthew Shardlow

Senior Lecturer at Manchester Metropolitan University

Daniel Ferrés

Post-Doctoral Research Assistant at LaSTUS Lab. at TALN-DTIC, Universitat Pompeu Fabra

Kai North

Ph.D. student at the Rochester Institute of Technology

Kim Cheng Sheang

PhD student at LaSTUS Lab. at TALN-DTIC, Universitat Pompeu Fabra

Program Committee (Tentative)

  • Raksha Agarwal (Indian Institute of Technology, India)
  • Sweta Agrawal (University of Maryland, USA)
  • Rodrigo Alarcón (Universidad Carlos III, Spain)
  • Oliver Alonzo (Rochester Institute of Technology,USA)
  • Fernando Alva Manchego (Cardiff University, UK)
  • Yuki Arase (Osaka University, Japan)
  • Susana Bautista (Universidad Francisco de Vitoria, Spain)
  • Remi Cardon (Université Catholique de Louvain, Belgium)
  • Felice Dell'Orletta (Istituto di Linguistica Computazionale “Antonio Zampolli”, Italy)
  • Anna Dmitrieva (University of Helsinki, Finland)
  • Sarah Ebling (University of Zurich, Switzerland)
  • Richard Evans (University of Wolverhampton, UK)
  • Nuria Gala (Université Aix-Marseille, France)
  • Itziar Gonzalez-Dios (University of the Basque Country, Spain)
  • Natalia Grabar (Université de Lille, France)
  • Raquel Hervás (Universidad Complutense de Madrid, Spain)
  • Tomoyuki Kajiwara (Ehime University, Japan)
  • Jaap Kamps (University of Amsterdam, Netherlands)
  • David Kauchak (Pomona College, USA)
  • Tannon Kew (University of Zurich, Switzerland)
  • Reno Kriz (University of Pennsylvania, USA)
  • Phillipe Laban (UC Berkeley, USA)
  • Bruce W. Lee (University of Pennsylvania, USA)
  • Mounica Maddela (Georgia Institute of Technology, USA)
  • Lourdes Moreno López (Universidad Carlos III,  Spain)
  • Christina Niklaus (University St. Gallen, Switzerland)
  • Nhung Nguyen (University of Manchester, UK)
  • Tadashi Nomoto (National Institute of Japanese Literature, Japan)
  • Brian Ondov (National Library of Medicine, USA)
  • Maja Popović (Dublin City University, Ireland)
  • Piotr Przybyła (ICS, Polish Academy of Sciences, Poland)
  • Jipeng Qiang (Yangzhou University, China)
  • Evelina Rennes (Linköping Universiy, Sweden)
  • Benoît Sagot (INRIA, France)
  • Regina Stodden (Heinrich Heine University, Germany)
  • Laura Vasquez Rodriguez (University of Manchester, UK)
  • Giulia Venturi (Istituto di Linguistica Computazionale “Antonio Zampolli”, Italy)
  • Gayatri Venugopal (Symbiosis International (Deemed University), India)
  • Daniel Wiechmann (University of Amsterdam, Netherlands)
  • Victoria Yaneva (National Board of Medical Examiners, USA)
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Reading

Books and Surveys

Tutorials

Papers

  • 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 (CHI '20). Association for Computing Machinery, New York, NY, USA, 1–13.
  • Sandra M. Aluísio, Lucia Specia, Thiago Alexandre Salgueiro Pardo, Erick Galani Maziero, Renata Pontin de Mattos Fortes. Towards Brazilian Portuguese automatic text simplification systems. ACM Symposium on Document Engineering 2008: 240-248
  • Dominique Brunato, Lorenzo De Mattei, Felice Dell'Orletta, Benedetta Iavarone, Giulia Venturi. Is this Sentence Difficult? Do you Agree? EMNLP 2018: 2690-2699
  • Stefan Bott, Luz Rello, Biljana Drndarevic, Horacio Saggion. Can Spanish Be Simpler? LexSiS: Lexical Simplification for Spanish. COLING 2012: 357-374
  • Or Biran, Samuel Brody, Noemie Elhadad. Putting it Simply: a Context-Aware Approach to Lexical Simplification. ACL (Short Papers) 2011: 496-501
  • William Coster, David Kauchak. Simple English Wikipedia: A New Text Simplification Task. ACL (Short Papers) 2011: 665-669
  • Thomas François, Núria Gala, Patrick Watrin, Cédrick Fairon. FLELex: a graded lexical resource for French foreign learners. LREC 2014: 3766-3773
  • Daniel Ferrés, Montserrat Marimon, Horacio Saggion, Ahmed AbuRa'ed. YATS: Yet Another Text Simplifier. NLDB 2016: 335-342
  • Goran Glavas, Sanja Štajner. Simplifying Lexical Simplification: Do We Need Simplified Corpora? ACL (2) 2015: 63-68
  • 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, pages 3536–3553.
  • Louis Martin, Éric de la Clergerie, Benoît Sagot, Antoine Bordes. Controllable Sentence Simplification. LREC 2020: 4689-4698
  • Lourdes Moreno, Rodrigo Alarcón, Paloma Martínez. EASIER system. Language resources for cognitive accessibility. ASSETS 2020: 65:1-65:3
  • Sergiu Nisioi, Sanja Štajner, Simone Paolo Ponzetto, Liviu P. Dinu. Exploring Neural Text Simplification Models. ACL (2) 2017: 85-91
  • OECD. 2013. OECD Skills Outlook 2013: First Results from the Survey of Adult Skills. Technical report, OECD Publishing.
  • Gustavo H. Paetzold, Lucia Specia. Unsupervised Lexical Simplification for Non-Native Speakers. AAAI 2016: 3761-3767
  • Advaith Siddharthan, Angrosh Mandya. Hybrid text simplification using synchronous dependency grammars with hand-written and automatically harvested rules. EACL 2014: 722-731
  • Carolina Scarton, Lucia Specia. Learning Simplifications for Specific Target Audiences. ACL (2) 2018: 712-718
  • Zhao, Sanqiang, et al. Integrating transformer and paraphrase rules for sentence simplification. EMNLP 2018: 3164-3173
  • Giulia Venturi, Tommaso Bellandi, Felice Dell'Orletta, Simonetta Montemagni. NLP-Based Readability Assessment of Health-Related Texts: a Case Study on Italian Informed Consent Forms. Louhi@EMNLP 2015: 131-141
  • Shardlow, Matthew, Richard J. Evans and Marcos Zampieri. “Predicting Lexical Complexity in English Texts.” ArXiv abs/2102.08773 (2021).
  • 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, pages 341–352, Aberdeen, Scotland, UK. Association for Computational Linguistics.
  • Sanja Štajner, Horacio Saggion, Simone Paolo Ponzetto. Improving lexical coverage of text simplification systems for Spanish. Expert Syst. Appl. 118: 80-91 (2019)
  • Sanja Štajner. 2021. Automatic text simplification for social good: Progress and challenges. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2637–2652.
  • Horacio Saggion, Elena Gómez-Martínez, Esteban Etayo, Alberto Anula, Lorena Bourg. Text Simplification in Simplext. Making Text More Accessible. Proces. del Leng. Natural 47: 341-342 (2011)
  • Sanja Štajner, Hanna Béchara, Horacio Saggion. A Deeper Exploration of the Standard PB-SMT Approach to Text Simplification and its Evaluation. ACL (2) 2015: 823-828
  • Elior Sulem, Omri Abend, Ari Rappoport. BLEU is Not Suitable for the Evaluation of Text Simplification. EMNLP 2018: 738-744
  • Elior Sulem, Omri Abend, Ari Rappoport. Semantic Structural Evaluation for Text Simplification. NAACL-HLT 2018: 685-696
  • Luz Rello, Ricardo Baeza-Yates, Stefan Bott, Horacio Saggion. Simplify or help?: text simplification strategies for people with dyslexia. W4A 2013: 15:1-15:10
  • Horacio Saggion, Sanja Štajner, Stefan Bott, Simon Mille, Luz Rello, Biljana Drndarevic. Making It Simplext: Implementation and Evaluation of a Text Simplification System for Spanish. ACM Trans. Access. Comput. 6(4): 14:1-14:36 (2015)
  • Advaith Siddharthan. A survey of research on text simplification. Recent Advances in Automatic Readability Assessment and Text Simplification. ITL - International Journal of Applied Linguistics, 165:2. 2014
  • Xu, W., Napoles, C., Pavlick, E., Chen, Q., & Callison-Burch, C. . Optimizing Statistical Machine Translation for Text Simplification. Transactions of the Association for Computational Linguistics, 4, 401–415. 2016.
  • 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, pages 584–594.

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