Generative Adversarial Networks for Text

• Martin Arjovsky, Soumith Chintala, and Leon Bottou. 2017. Wasserstein generative adversarial networks. In Doina Precup and Yee Whye Teh, editors, Proceedings of the 34th International Conference on Ma- chine Learning. PMLR, International Convention Centre, Sydney, Australia, volume 70 of Proceedings of Machine Learning Research, pages 214-223. [link].
• Xilun Chen, Yu Sun, Ben Athiwaratkun, Claire Cardie, and Kilian Weinberger. 2018. Adversarial deep averaging networks for cross-lingual sentiment classification. Transactions of the Association for Computational Linguistics 6:557-570. [link].
• B. Dai, S. Fidler, R. Urtasun, and D. Lin. 2017. Towards diverse and natural image descriptions via a conditional gan. In 2017 IEEE International Conference on Computer Vision (ICCV). pages 2989- 2998. [link].
• Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems 27 , Curran Associates, Inc., pages 2672-2680. [link].
• Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P. Xing. 2017. Toward con- trolled generation of text. In Proceedings of the 34th International Conference on Machine Learning. PMLR, International Convention Centre, Sydney, Australia, volume 70 of Proceedings of Machine Learning Research, pages 1587-1596. [link].
• Jiwei Li, Will Monroe, Tianlin Shi, S_ebastien Jean, Alan Ritter, and Dan Jurafsky. 2017. Adversarial learning for neural dialogue generation. In Proceedings of the 2017 Conference on Empirical Meth- ods in Natural Language Processing. Association for Computational Linguistics, pages 2157-2169. [link].
• Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2017. Adversarial multi-task learning for text classification. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, pages 1-10. [link].
• William Lotter, Gabriel Kreiman, and David D. Cox. 2015. Unsupervised learning of visual structure using predictive generative networks. CoRR abs/1511.06380. [link].
• Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved techniques for training gans. In Proceedings of the 30th International Conference on Neural Information Processing Systems. Curran Associates Inc., USA, NIPS'16, pages 2234-2242. [link].
• Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2017. Style transfer from non-parallel text by cross-alignment. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vish- wanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30 , Curran Associates, Inc., pages 6830-6841. [link].
• Sandeep Subramanian, Sai Rajeswar, Francis Dutil, Chris Pal, and Aaron Courville. 2017. Adversarial generation of natural language. In Proceedings of the 2nd Workshop on Representation Learning for NLP. Association for Computational Linguistics, pages 241-251. [link].
• Lijun Wu, Yingce Xia, Fei Tian, Li Zhao, Tao Qin, Jianhuang Lai, and Tie-Yan Liu. 2018. Adversarial neural machine translation. In Jun Zhu and Ichiro Takeuchi, editors, Proceedings of The 10th Asian Conference on Machine Learning. PMLR, volume 95 of Proceedings of Machine Learning Research, pages 534-549. [link].
• Zhen Yang, Wei Chen, Feng Wang, and Bo Xu. 2018. Improving neural machine translation with conditional sequence generative adversarial nets. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume 1 (Long Papers). Association for Computational Linguistics, pages 1346-1355. [link].
• Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2016. Seqgan: Sequence generative adversarial nets with policy gradient. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). [link].

Writing Revisions

• Tazin Afrin and Diane Litman. 2018. Annotation and classification of sentence-level revision improve- ment. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications. New Orleans, Louisiana, pages 240-246. [link].
• Regina Barzilay and Noemie Elhadad. 2003. Sentence alignment for monolingual comparable cor- pora. In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Process- ing. Association for Computational Linguistics, Stroudsburg, PA, USA, EMNLP '03, pages 25-32. [link].
• Amit Bronner and Christof Monz. 2012. User edits classification using document revision histories. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, Avignon, France, EACL '12, pages 356-366. [link].
• Johannes Daxenberger and Iryna Gurevych. 2012. A corpus-based study of edit categories in featured and non-featured wikipedia articles. In Proceedings of the 24th International Conference on Computational Linguistics. Mumbai, India, COLING '12, pages 711-726. [link].
• Johannes Daxenberger and Iryna Gurevych. 2013. Automatically classifying edit categories in wikipedia revisions. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Pro- cessing. Association for Computational Linguistics, Seattle, Washington, USA, EMNLP '13, pages 578-589. [link].
• Lester Faigley and Stephen Witte. 1981. Analyzing revision. College Composition and Communication 32(4):400-414. [link].
• Manaal Faruqui, Ellie Pavlick, Ian Tenney, and Dipanjan Das. 2018. Wikiatomicedits: A multilingual corpus of wikipedia edits for modeling language and discourse. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pages 305-315. [link].
• Homa B. Hashemi and Christian D. Schunn. 2014. A tool for summarizing students' changes across drafts. In Intelligent Tutoring Systems. Springer International Publishing, Cham, pages 679-682. [link].
• Jonas Mueller, David Gifford, and Tommi Jaakkola. 2017. Sequence to better sequence: Continuous revision of combinatorial structures. In Proceedings of the 34th International Conference on Machine Learning. PMLR, International Convention Centre, Sydney, Australia, volume 70 of Proceedings of Machine Learning Research, pages 2536-2544. [link].
• Rani Nelken and Stuart M. Shieber. 2006. Towards robust context-sensitive sentence alignment for mono- lingual corpora. In 11th Conference of the European Chapter of the Association for Computational Linguistics. http://aclweb.org/anthology/E06-1021">[link].
• Antonette Shibani, Simon Knight, and Simon Buckingham Shum. 2018. Understanding re- visions in student writing through revision graphs. In International Conference on Ar- tificial Intelligence in Education. Springer International Publishing, Cham, pages 332-336. [link].
• Chenhao Tan and Lillian Lee. 2014. A corpus of sentence-level revisions in academic writing: A step towards understanding statement strength in communication. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Baltimore, MD, USA, volume 2: Short Papers, pages 403-408. [link].
• Ping Ping Tan, Karin Verspoor, and Tim Miller. 2015. Structural alignment as the basis to improve significant change detection in versioned sentences. In Proceedings of the Australasian Language Technology Association Workshop 2015 . pages 101-109. [link].
• Charissa Tansomboon, Libby F. Gerard, Jonathan M. Vitale, and Marcia C. Linn. 2017. Designing automated guidance to promote productive revision of science explanations. International Journal of Artificial Intelligence in Education 27(4):729-757. [link].
• Elif Yamangil and Rani Nelken. 2008. Mining wikipedia revision histories for improving sentence com- pression. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers. Association for Computational Linguistics, Strouds- burg, PA, USA, HLT-Short '08, pages 137-140. [link].
• Diyi Yang, Aaron Halfaker, Robert E. Kraut, and Eduard H. Hovy. 2016. Who did what: Editor role identification in wikipedia. In Tenth International AAAI Conference on Web and Social Media. AAAI Press, ICWSM, pages 446-455. [link].
• Fan Zhang, Rebecca Hwa, Diane Litman, and Homa B. Hashemi. 2016. Argrewrite: A web-based revision assistant for argumentative writings. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations. Association for Computational Linguistics, San Diego, California, pages 37-41 [link].
• Fan Zhang and Diane Litman. 2014. Sentence-level rewriting detection. In Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications. Association for Compu- tational Linguistics, pages 149-154. [link].
• Fan Zhang and Diane Litman. 2015. Annotation and classication of argumentative writing re- visions. In Proceedings of the 10th Workshop on Innovative Use of NLP for Building Educa- tional Applications. Association for Computational Linguistics, Denver, Colorado, pages 133-143. [link].

Grammar Error Correction

• EM Bender and Dan Flickinger. 2004. Arboretum: Using a precision grammar for grammar checking in CALL. InSTIL/ICALL . . . 17:19. [link].
• Chris Brockett, William B. Dolan, and Michael Gamon. 2006. Correcting ESL errors using phrasal SMT techniques. Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06 pages 249-256. [link].
• Martin Chodorow and Claudia Leacock. 2000. An unsupervised method for detecting grammatical errors. Proceedings of the 1st annual meeting of the North American chapter of the Association for Computational Linguistics pages 140-147. [link].
• Daniel Dahlmeier, Hwee Tou Ng, and Eric Jun Feng Ng. 2012. NUS at the HOO 2012 shared task. In Proceedings of the Seventh Workshop on Building Educational Applications Using NLP. Asso- ciation for Computational Linguistics, Stroudsburg, PA, USA, NAACL HLT '12, pages 216-224. [link].
• Daniel Dahlmeier, Hwee Tou Ng, and Siew MeiWu. 2013. Building a Large Annotated Corpus of Learner English: The NUS Corpus of Learner English. In Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics, At- lanta, Georgia, pages 22-31. [link].
• Robert Dale, Ilya Anisimoff, and George Narroway. 2012. Hoo 2012: A report on the preposition and determiner error correction shared task. In Proceedings of the Seventh Workshop on Build- ing Educational Applications Using NLP. Association for Computational Linguistics, pages 54-62. [link].
• Vidas Daudaravicius, Rafael E. Banchs, Elena Volodina, and Courtney Napoles. 2016. A report on the automatic evaluation of scientific writing shared task. In Proceedings of the 11th Workshop on Inno- vative Use of NLP for Building Educational Applications. Association for Computational Linguistics, pages 53-62. [link].
• Mariano Felice, Zheng Yuan, Oistein E. Andersen, Helen Yannakoudakis, and Ekaterina Kochmar. 2014. Grammatical error correction using hybrid systems and type filtering. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task. Association for Computa- tional Linguistics, pages 15-24. [link].
• Michael Gamon. 2011. High-order sequence modeling for language learner error detection. In Pro- ceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications. As- sociation for Computational Linguistics, Stroudsburg, PA, USA, IUNLPBEA '11, pages 180-189. [link].
• Roman Grundkiewicz and Marcin Junczys-Dowmunt. 2018. Near human-level performance in gram- matical error correction with hybrid machine translation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). Association for Computational Linguistics, pages 284-290. [link].
• Marcin Junczys-Dowmunt and Roman Grundkiewicz. 2016. Phrase-based machine translation is state- of-the-art for automatic grammatical error correction. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pages 1546-1556. [link].
• Nitin Madnani, J. Tetreault, Martin Chodorow, and A. Rozovskaya. 2011. They can help: using crowd- sourcing to improve the evaluation of grammatical error detection systems. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2 pages 508-513. [link].
• Courtney Napoles, Keisuke Sakaguchi, and Joel Tetreault. 2017. J eg: A fluency corpus and benchmark for grammatical error correction. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. Association for Computational Linguistics, pages 229-234. [link].
• Hwee Tou Ng, Siew Mei Wu, Ted Briscoe, Christian Hadiwinoto, Raymond Hendy Susanto, and Christo- pher Bryant. 2014. The conll-2014 shared task on grammatical error correction. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task. Association for Computational Linguistics, pages 1-14. [link].
• Diane Nicholls. 2003. The Cambridge Learner Corpus - error coding and analysis for lexicography and ELT Diane Nicholls Cambridge University Press ( freelance ). Summer Workshop on Learner Corpora pages 572-581. [link].
• Alla Rozovskaya and Dan Roth. 2011. Algorithm selection and model adaptation for esl correction tasks. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguis- tics: Human Language Technologies. Association for Computational Linguistics, pages 924-933. [link].
• Alla Rozovskaya and Dan Roth. 2013. Joint learning and inference for grammatical error correction. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pages 791-802. [link].
• Keisuke Sakaguchi, Courtney Napoles, and Joel Tetreault. 2017a. Gec into the future: Where are we going and how do we get there? In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics, pages 180-187. [link].
• Keisuke Sakaguchi, Matt Post, and Benjamin Van Durme. 2017b. Grammatical error correction with neural reinforcement learning. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Asian Federation of Natural Language Processing, pages 366-372. [link].
• Allen Schmaltz, Yoon Kim, Alexander M. Rush, and Stuart Shieber. 2016. Sentence-level grammatical error identification as sequence-to-sequence correction. In Proceedings of the 11th Workshop on Inno- vative Use of NLP for Building Educational Applications. Association for Computational Linguistics, pages 242-251. [link].
• Toshikazu Tajiri, Mamoru Komachi, and Yuji Matsumoto. 2012. Tense and aspect error correction for esl learners using global context. In Proceedings of the 50th Annual Meeting of the Association for Com- putational Linguistics: Short Papers - Volume 2 . Association for Computational Linguistics, Strouds- burg, PA, USA, ACL '12, pages 198-202. [link].
• Huichao Xue and Rebecca Hwa. 2014. Improved Correction Detection in Revised ESL Sentences. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Baltimore, Maryland, pages 599-604. [link].
• Helen Yannakoudakis, Ted Briscoe, and Ben Medlock. 2011. A New Dataset and Method for Auto- matically Grading ESOL Texts. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Portland, Oregon, volume 1 of HLT '11 , pages 180-189. [link].
• Zheng Yuan and Ted Briscoe. 2016. Grammatical error correction using neural machine translation. Pro- ceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies pages 380-386. [link].

Copyright © Tazin Afrin