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| "title": "Challenges in Measuring Bias via Open-Ended Language Generation", | |
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| "text": "This volume contains the proceedings of the Fourth Workshop on Gender Bias in Natural Language Processing, held in conjunction with the 2022 Conference of the North American Chapter of the Association for Computational Linguistics -Human Language Technologies (NAACL-HLT2022) . This year, the organization committee changed membership: Kellie Webster made way for Christine Basta and Gabriel Stanovsky. Kellie has been one of the main reasons for the success of this workshop and we would like to thank her for her valuable and enthusiastic contribution to this workshop. We are glad to welcome our two co-organizers and look forward to sharing their insights and expertise.", | |
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| "text": "(NAACL-HLT2022)", | |
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| "section": "Preface", | |
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| "text": "This year, the workshop received 33 submissions of technical papers (12 long papers, 21 short papers), of which 28 were accepted (11 long, 17 short), for an acceptance rate of 84%. We are pleased to see an increased interest compared to our previous editions in the last three years: the submissions have increased this year to 33 papers compared to 18 papers last year and 19 papers in 2019 and 2020. Furthermore, the high quality of the submissions allowed us to have a higher acceptance rate this year of 84% compared to the previous years, where the acceptance rate was 63%, 68% and 67% respectively. Once more, we thank the Programme Committee members, who provided extremely valuable reviews in terms of technical content and bias statements, for the high-quality selection of research works.", | |
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| "text": "The accepted papers cover a wide range of natural language processing research areas. From the core tasks of NLP, the papers include language modeling and generation, annotation, machine translation, word embeddings, and evaluation. New aspects regarding the analysis and the debiasing mechanisms are introduced and we are excited about the discussions these will inspire. Besides English, we have interesting studies targeting Inuktikut, Hindi and Marathi as well as Chinese, Italian, French and Spanish. All papers cover a variety of gender (and intersectional) bias studies as well as a taxonomy definition.", | |
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| "text": "Finally, the workshop has two keynotes by speakers of high standing: Kellie Webster and Kevin Robinson, Google Research, and Kai-Wei Chang, University of California (UCLA-CS). We also have a panel under the theme of Evaluating gender bias in NLP and we are looking forward to the insights of this panel.", | |
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| "text": "We are very pleased to keep the high interest that this workshop has generated over the last three editions and we look forward to an enriching discussion on how to address bias problems in NLP applications when we meet at a hybrid event on 15 July 2022! ", | |
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| "ref_id": "b1", | |
| "title": "Swetha Gangu, Alan W. Black and Emma Strubell Choose Your Lenses: Flaws in Gender Bias Evaluation Hadas Orgad and Yonatan Belinkov", | |
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| "volume": "16", | |
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| "pages": "30--47", | |
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| "raw_text": "Evaluating Gender Bias Transfer from Film Data Amanda Bertsch, Ashley Oh, Sanika Natu, Swetha Gangu, Alan W. Black and Emma Strubell Choose Your Lenses: Flaws in Gender Bias Evaluation Hadas Orgad and Yonatan Belinkov 16:30 -17:00 Discussion and closing x", | |
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| "text": "July 2022Christine Basta, Marta R. Costa-juss\u00e0, Hila Gonen, Christian Hardmeier and Gabriel StanovskyChristian Hardmeier was supported by the Swedish Research Council under grant 2017-930.", | |
| "content": "<table><tr><td>Organizing Committee Program Committee</td></tr><tr><td>Organizers Chairs</td></tr><tr><td>Christine Basta, Polytechnic University of Catalonia and Alexandria University Christine Basta, Universitat Polit\u00e8cnica de Catalunya</td></tr><tr><td>Marta R. Costa-juss\u00e0, Meta AI Marta R. Costa-juss\u00e0, Meta AI</td></tr><tr><td>Hila Gonen, Meta AI and University of Washington Hila Gonen, Meta AI and University of Washington</td></tr><tr><td>Christian Hardmeier, IT University of Copenhagen / Uppsala University Christian Hardmeier, IT University of Copenhagen/Uppsala University</td></tr><tr><td>Gabriel Stanovsky, Hebrew University of Jerusalem Gabriel Stanovsky, The Hebrew University of Jerusalem</td></tr><tr><td>Program Committee</td></tr><tr><td>Gavin Abercrombie, Heriot Watt University</td></tr><tr><td>Jenny Bj\u00f6rklund, Uppsala University</td></tr><tr><td>Su Lin Blodgett, Microsoft Research</td></tr><tr><td>Houda Bouamor, Carnegie Mellon University in Qatar</td></tr><tr><td>Ryan Cotterell, ETH Z\u00fcrich</td></tr><tr><td>Hannah Devinney, Ume\u00e5 University</td></tr><tr><td>Matthias Gall\u00e9, Naver Labs Europe</td></tr><tr><td>Mercedes Garc\u00eda-Mart\u00ednez, Pangeanic</td></tr><tr><td>Seraphina Goldfarb-Tarrant, University of Edinburgh</td></tr><tr><td>Zhengxian Gong, Computer science and technology school,soochow university</td></tr><tr><td>Nizar Habash, New York University Abu Dhabi</td></tr><tr><td>Ben Hachey, Harrison.ai</td></tr><tr><td>Svetlana Kiritchenko, National Research Council Canada</td></tr><tr><td>Shiyang Li, UC Santa Barbara</td></tr><tr><td>Tomasz Limisiewicz, Charles University in Prague</td></tr><tr><td>Gili Lior, The Hebrew University of Jerusalem</td></tr><tr><td>Sharid Lo\u00e1iciga, University of Gothenburg</td></tr><tr><td>Inbal Magar, The Hebrew University of Jeruslaem</td></tr><tr><td>Maite Melero, BSC</td></tr><tr><td>Johanna Monti, L'Orientale University of Naples</td></tr><tr><td>Carla Perez Almendros, Cardiff University</td></tr><tr><td>Will Radford, Canva</td></tr><tr><td>Rafal Rzepka, Hokkaido University</td></tr><tr><td>Sonja Schmer-Galunder, Smart Information Flow Technologies</td></tr><tr><td>Bonnie Webber, University of Edinburgh</td></tr><tr><td>Lilja \u00d8vrelid, Dept of Informatics, University of Oslo</td></tr></table>" | |
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| "text": "Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes Antonis Maronikolakis, Philip Baader and Hinrich Sch\u00fctze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings", | |
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