{"type":"ScholarlyArticle","title":"iRe2f: Rethinking Effective Refinement in Language Structure Prediction via Efficient Iterative Retrospecting and Reasoning","authors":[{"name":"Zuchao Li","slug":"zuchao-li","given":"Zuchao","family":"Li","affiliation":"National Engineering Research Center for Multimedia Software ,; Hubei Luojia Laboratory , Wuhan 430072 , P. R. China"},{"name":"Xingyi Guo","slug":"xingyi-guo","given":"Xingyi","family":"Guo","affiliation":"National Engineering Research Center for Multimedia Software ,"},{"name":"Letian Peng","slug":"letian-peng","given":"Letian","family":"Peng","affiliation":"Computer Science and Engineering , University of California San Diego"},{"name":"Lefei Zhang","slug":"lefei-zhang","given":"Lefei","family":"Zhang","affiliation":"National Engineering Research Center for Multimedia Software ,; Hubei Luojia Laboratory , Wuhan 430072 , P. R. China; School of Computer Science , Wuhan University , Wuhan , 430072 , P. R. China"},{"name":"Hai Zhao","slug":"hai-zhao","given":"Hai","family":"Zhao","affiliation":"Department of Computer Science and Engineering , Shanghai Jiao Tong University"}],"abstract":"Refinement plays a critical role in language structure prediction, a process that deals with complex situations such as structural edge interdependencies. Since language structure prediction usually modeled as graph parsing, typical refinement methods involve taking an initial parsing graph as input and refining it using language input and other relevant information. Intuitively, a refinement component, i.e., refiner, should be lightweight and efficient, as it is only responsible for correcting faults in the initial graph. However, current refiners add a significant burden to the parsing process due to their reliance on time-consuming encoding-decoding procedure on the language input and graph. To make the refiner more practical for real-world applications, this paper proposes a lightweight but effective iterative refinement framework, iRe^2f, based on iterative retrospecting and reasoning without involving the re-encoding process on the graph. iRe^2f iteratively refine the parsing graph based on interaction between graph and sequence and efficiently learns the shortcut to update the sequence and graph representations in each iteration.  The shortcut is calculated based on the graph representation in the latest iteration.  iRe^2f reduces the number of refinement parameters by 90% compared to the previous smallest refiner. Experiments on a variety of language structure prediction tasks show that iRe^2f performs comparably or better than current state-of-the-art refiners, with a significant increase in efficiency.","doi":"10.24963/ijcai.2023/570","url":"https://ijcai.org/proceedings/2023/570","pdf":"https://ijcai.org/proceedings/2023/0570.pdf","markdown":"https://ijcai.org/proceedings/2023/570.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/570","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Natural Language Processing","track":"Main Track","number":570,"pages":{"first":5131,"last":5140},"keywords":["Natural Language Processing: NLP: Tagging, chunking, and parsing","Natural Language Processing: NLP: Interpretability and analysis of models for NLP"],"videos":[],"references":[{"position":1,"raw":"[Almeida and Martins, 2015] Mariana S. C. Almeida and André F. T. Martins. Lisbon: Evaluating turbosemantic- parser on multiple languages and out-of-domain data. In","title":"Lisbon: Evaluating turbosemanticparser on multiple languages and out-of-domain data","authors":"S C Mariana, Almeida, F T André, Martins","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"Daniel M. Cer, David Jurgens, Preslav Nakov, and Torsten Zesch, editors, Proceedings of the 9th International Work- shop on Semantic Evaluation, SemEval@NAACL-HLT 2015, Denver, Colorado, USA, June 4-5, 2015, pages 970- 973, 2015.","title":"Proceedings of the 9th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2015","authors":null,"year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bocharova, 2008] V Bocharova. Rule-based language structure prediction. Natural Language Engineering, 14(3):299-321, 2008.","title":"Rule-based language structure prediction","authors":"Bocharova","year":"2008","venue":"Natural Language Engineering","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bojanowski et al., 2017] Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. Enriching word vec- tors with subword information. Transactions of the Asso- ciation for Computational Linguistics, 5:135-146, 2017.","title":"Enriching word vectors with subword information","authors":"Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov","year":"2017","venue":"Transactions of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen and Manning, 2014] Dan Chen and Christopher D Manning. A fast and accurate dependency parser using neural networks. In Proceedings of EMNLP, pages 740- 750, 2014.","title":"A fast and accurate dependency parser using neural networks","authors":"Dan Chen, Christopher D Manning","year":"2014","venue":"Proceedings of EMNLP","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Clark et al., 2018] Kevin Clark, Minh-Thang Luong, Christopher D. Manning, and Quoc Le. Semi-supervised sequence modeling with cross-view training. In Pro- ceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1914-1925, October-November 2018.","title":"Semi-supervised sequence modeling with cross-view training","authors":"Kevin Clark, Minh-Thang Luong, Christopher D Manning, Quoc Le","year":"2018","venue":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Dozat and Manning, 2017] Timothy Dozat and Christo- pher D. Manning. Deep biaffine attention for neural depen- dency parsing. In 5th International Conference on Learn- ing Representations, ICLR 2017, Toulon, France, April 24- 26, 2017, Conference Track Proceedings, 2017.","title":"Deep biaffine attention for neural dependency parsing","authors":"Timothy Dozat, Christopher D Manning","year":"2017","venue":"5th International Conference on Learning Representations, ICLR 2017","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Dozat and Manning, 2018] Timothy Dozat and Christo- pher D. Manning. Simpler but more accurate semantic de- pendency parsing. In Iryna Gurevych and Yusuke Miyao, editors, Proceedings of the 56th Annual Meeting of the As- sociation for Computational Linguistics, ACL 2018, Mel- bourne, Australia, July 15-20, 2018, Volume 2: Short Pa- pers, pages 484-490, 2018.","title":"Simpler but more accurate semantic dependency parsing","authors":"Timothy Dozat, Christopher D Manning","year":"2018","venue":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Du et al., 2015] Yantao Du, Fan Zhang, Xun Zhang, Wei- wei Sun, and Xiaojun Wan. Peking: Building semantic dependency graphs with a hybrid parser. In Daniel M. Cer, David Jurgens, Preslav Nakov, and Torsten Zesch, editors, Proceedings of SemEval@NAACL-HLT, pages 927-931, 2015.","title":"Peking: Building semantic dependency graphs with a hybrid parser","authors":"Yantao Du, Fan Zhang, Xun Zhang, Weiwei Sun, Xiaojun Wan","year":"2015","venue":"Proceedings of SemEval@NAACL-HLT","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Fernández-González and Gómez-Rodríguez, 2019] Daniel Fernández-González and Carlos Gómez-Rodríguez. Left-to-right dependency parsing with pointer networks. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 710-716, Minneapolis, Minnesota, June 2019.","title":"Left-to-right dependency parsing with pointer networks","authors":"Fernández-González, Gómez-Rodríguez ; Daniel Fernández-González, Carlos Gómez-Rodríguez","year":"2019","venue":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gui et al., 2020] Tao Gui, Jiacheng Ye, Qi Zhang, Zhengyan Li, Zichu Fei, Yeyun Gong, and Xuanjing Huang. Uncertainty-aware label refinement for sequence labeling. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu, editors, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pages 2316-2326, 2020.","title":"Uncertainty-aware label refinement for sequence labeling","authors":"Tao Gui, Jiacheng Ye, Qi Zhang, Zhengyan Li, Zichu Fei, Yeyun Gong, Xuanjing Huang","year":"2020","venue":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hajic et al., 2009] Jan Hajic, Massimiliano Ciaramita, Richard Johansson, Daisuke Kawahara, Maria Antònia Martí, Lluís Màrquez, Adam Meyers, Joakim Nivre, Sebastian Padó, Jan Stepánek, Pavel Stranák, Mihai Surdeanu, Nianwen Xue, and Yi Zhang. The conll-2009 shared task: Syntactic and semantic dependencies in multiple languages. In Jan Hajic, editor, Proceedings of the CoNLL, pages 1-18, 2009.","title":"The conll-2009 shared task: Syntactic and semantic dependencies in multiple languages","authors":"Jan Hajic, Massimiliano Ciaramita, Richard Johansson, Daisuke Kawahara, Maria Antònia Martí, Lluís Màrquez, Adam Meyers, Joakim Nivre, Sebastian Padó, Jan Stepánek, Pavel Stranák, Mihai Surdeanu, Nianwen Xue, Yi Zhang","year":"2009","venue":"Proceedings of the CoNLL","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hashimoto et al., 2016] Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, and Richard Socher. A joint many-task model: Growing a neural network for multiple NLP tasks. CoRR, abs/1611.01587, 2016.","title":"A joint many-task model: Growing a neural network for multiple NLP tasks","authors":"Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, Richard Socher","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[He et al., 2019] Shexia He, Zuchao Li, and Hai Zhao. Syntax-aware multilingual semantic role labeling. In Pro- ceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Interna- tional Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5350-5359, November 2019.","title":"Syntax-aware multilingual semantic role labeling","authors":"He","year":"2019","venue":"Pro-ceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Hua and Wang, 2020] Xinyu Hua and Lu Wang. PAIR: planning and iterative refinement in pre-trained transform- ers for long text generation. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu, editors, Proceedings of EMNLP, pages 781-793, 2020.","title":"PAIR: planning and iterative refinement in pre-trained transformers for long text generation","authors":"Xinyu Hua, Lu Wang","year":"2020","venue":"Proceedings of EMNLP","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Ji et al., 2019] Tao Ji, Yuanbin Wu, and Man Lan. Graph- based dependency parsing with graph neural networks. In Proceedings of the 57th Annual Meeting of the Associa- tion for Computational Linguistics, pages 2475-2485, July 2019.","title":"Graphbased dependency parsing with graph neural networks","authors":"Tao Ji, Yuanbin Wu, Man Lan","year":"2019","venue":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Jia et al., 2020] Zixia Jia, Youmi Ma, Jiong Cai, and Kewei Tu. Semi-supervised semantic dependency parsing using CRF autoencoders. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6795-6805, July 2020.","title":"Semi-supervised semantic dependency parsing using CRF autoencoders","authors":"Jia","year":"2020","venue":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kingma and Ba, 2015] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Yoshua Bengio and Yann LeCun, editors, 3rd International Con- ference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Pro- ceedings, 2015.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2015","venue":"3rd International Conference on Learning Representations, ICLR 2015","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Kiperwasser and Goldberg, 2016] Eliyahu Kiperwasser and Yoav Goldberg. Simple and accurate dependency parsing using bidirectional LSTM feature representations. Trans. Assoc. Comput. Linguistics, 4:313-327, 2016.","title":"Eliyahu Kiperwasser and Yoav Goldberg. Simple and accurate dependency parsing using bidirectional LSTM feature representations","authors":"Goldberg Kiperwasser","year":"2016","venue":"Trans. Assoc. Comput. Linguistics","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Kurita and Søgaard, 2019] Shuhei Kurita and Anders Søgaard. Multi-task semantic dependency parsing with policy gradient for learning easy-first strategies. In Pro- ceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2420-2430, Florence, Italy, July 2019.","title":"Multi-task semantic dependency parsing with policy gradient for learning easy-first strategies","authors":"Søgaard ; ] Shuhei Kurita, Anders Kurita, Søgaard","year":"2019","venue":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Lee et al., 2020] Jason Lee, Raphael Shu, and Kyunghyun Cho. Iterative refinement in the continuous space for non- autoregressive neural machine translation. In Bonnie Web- ber, Trevor Cohn, Yulan He, and Yang Liu, editors, Pro- ceedings of EMNLP, pages 1006-1015, 2020.","title":"Iterative refinement in the continuous space for nonautoregressive neural machine translation","authors":"Jason Lee, Raphael Shu, Kyunghyun Cho","year":"2020","venue":"Proceedings of EMNLP","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Li et al., 2020] Zuchao Li, Hai Zhao, Rui Wang, and Kevin Parnow. High-order semantic role labeling. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1134-1151, November 2020.","title":"High-order semantic role labeling","authors":"Zuchao Li, Hai Zhao, Rui Wang, Kevin Parnow","year":"2020","venue":"Findings of the Association for Computational Linguistics: EMNLP 2020","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Li et al., 2021] Zuchao Li, Hai Zhao, Shexia He, and Jiaxun Cai. Syntax role for neural semantic role labeling. Com- put. Linguistics, 47(3):529-574, 2021.","title":"Syntax role for neural semantic role labeling","authors":"Li","year":"2021","venue":"Comput. Linguistics","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Li et al., 2022a] Zuchao Li, Zhuosheng Zhang, Hai Zhao, Rui Wang, Kehai Chen, Masao Utiyama, and Eiichiro Sumita. Text compression-aided transformer encoding. IEEE Trans. Pattern Anal. Mach. Intell., 44(7):3840-3857, 2022.","title":"Text compression-aided transformer encoding","authors":"Li","year":"2022","venue":"IEEE Trans. Pattern Anal. Mach. Intell","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Li et al., 2022b] Zuchao Li, Junru Zhou, Hai Zhao, Zhisong Zhang, Haonan Li, and Yuqi Ju. Neural character-level syntactic parsing for chinese. J. Artif. Intell. Res., 73:461- 509, 2022.","title":"Neural character-level syntactic parsing for chinese","authors":"Li","year":"2022","venue":"J. Artif. Intell. Res","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Lyu et al., 2019a] Chunchuan Lyu, Shay B. Cohen, and Ivan Titov. Semantic role labeling with iterative structure refinement. In Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan, editors, Proceedings of EMNLP-IJCNLP, pages 1071-1082, 2019.","title":"Semantic role labeling with iterative structure refinement","authors":"Lyu","year":"2019","venue":"Proceedings of EMNLP-IJCNLP","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Lyu et al., 2019b] Chunchuan Lyu, Shay B. Cohen, and Ivan Titov. Semantic role labeling with iterative structure refinement. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Lan- guage Processing (EMNLP-IJCNLP), pages 1071-1082, November 2019.","title":"Semantic role labeling with iterative structure refinement","authors":"Lyu","year":"2019","venue":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Ma et al., 2018] Xuezhe Ma, Zecong Hu, Jingzhou Liu, Nanyun Peng, Graham Neubig, and Eduard Hovy. Stack- pointer networks for dependency parsing. In Proceedings of the 56th Annual Meeting of the Association for Compu- tational Linguistics (Volume 1: Long Papers), pages 1403- 1414, July 2018.","title":"Stackpointer networks for dependency parsing","authors":"Ma","year":"2018","venue":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Marcheggiani et al., 2017] Diego Marcheggiani, Anton Frolov, and Ivan Titov. A simple and accurate syntax- agnostic neural model for dependency-based semantic role labeling. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 411-420, August 2017.","title":"A simple and accurate syntaxagnostic neural model for dependency-based semantic role labeling","authors":"Marcheggiani","year":"2017","venue":"Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Marcus et al., 1993] Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19(2):313-330, 1993.","title":"Building a large annotated corpus of English: The Penn Treebank","authors":"Mitchell P Marcus, Beatrice Santorini, Mary Ann Marcinkiewicz","year":"1993","venue":"Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Mohammadshahi and Henderson, 2021] Alireza Mo- hammadshahi and James Henderson. Recursive non- autoregressive graph-to-graph transformer for dependency parsing with iterative refinement. Trans. Assoc. Comput. Linguistics, 9:120-138, 2021.","title":"Recursive nonautoregressive graph-to-graph transformer for dependency parsing with iterative refinement","authors":"Alireza Mohammadshahi, James Henderson","year":"2021","venue":"Trans. Assoc. Comput. Linguistics","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Mulcaire et al., 2018] Phoebe Mulcaire, Swabha Swayamdipta, and Noah A. Smith. Polyglot seman- tic role labeling. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 667-672, July 2018.","title":"Phoebe Mulcaire, Swabha Swayamdipta, and Noah A. Smith. Polyglot semantic role labeling","authors":"Mulcaire","year":"2018","venue":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Nivre et al., 2009] Joakim Nivre, Wim Peters, and Ivan Titov. Statistical dependency parsing: An introduction. Synthesis Lectures on Human Language Technologies, 2(1):1-175, 2009.","title":"Statistical dependency parsing: An introduction","authors":"Nivre","year":"2009","venue":"Synthesis Lectures on Human Language Technologies","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Oepen et al., 2015] Stephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Silvie Cinková, Dan Flickinger, Jan Hajic, and Zdenka Uresová. Semeval 2015 task 18: Broad-coverage semantic dependency parsing. In","title":"Semeval 2015 task 18: Broad-coverage semantic dependency parsing","authors":"Stephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Silvie Cinková, Dan Flickinger, Jan Hajic, Zdenka Uresová","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"Daniel M. Cer, David Jurgens, Preslav Nakov, and Torsten Zesch, editors, Proceedings of SemEval@NAACL-HLT, pages 915-926, 2015.","title":"Proceedings of SemEval@NAACL-HLT","authors":null,"year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Peng et al., 2017] Hao Peng, Sam Thomson, and Noah A. Smith. Deep multitask learning for semantic dependency parsing. In Regina Barzilay and Min-Yen Kan, editors, Proceedings of ACL, pages 2037-2048, 2017.","title":"Deep multitask learning for semantic dependency parsing","authors":"Hao Peng, Sam Thomson, Noah A Smith","year":"2017","venue":"Proceedings of ACL","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Pennington et al., 2014] Jeffrey Pennington, Richard Socher, and Christopher Manning. GloVe: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532-1543, October 2014.","title":"GloVe: Global vectors for word representation","authors":"Jeffrey Pennington, Richard Socher, Christopher Manning","year":"2014","venue":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Sun et al., 2021] Kailai Sun, Zuchao Li, and Hai Zhao. Multilingual pre-training with universal dependency learn- ing. In Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan, editors, NeurIPS, pages 8444-8456, 2021.","title":"Multilingual pre-training with universal dependency learning","authors":"Kailai Sun, Zuchao Li, Hai Zhao","year":"2021","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vish- wanathan, and Roman Garnett, editors, NeurIPS, pages 5998-6008, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, Illia Polosukhin","year":"2017","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wang and Tu, 2020] Xinyu Wang and Kewei Tu. Second- order neural dependency parsing with message passing and end-to-end training. In Kam-Fai Wong, Kevin Knight, and Hua Wu, editors, Proceedings of AACL/IJCNLP, pages 93-99, 2020.","title":"Secondorder neural dependency parsing with message passing and end-to-end training","authors":"Xinyu Wang, Kewei Tu","year":"2020","venue":"Proceedings of AACL/IJCNLP","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Wang et al., 2018] Yuxuan Wang, Wanxiang Che, Jiang Guo, and Ting Liu. A neural transition-based approach for semantic dependency graph parsing. In Sheila A. McIl- raith and Kilian Q. Weinberger, editors, Proceedings of AAAI, pages 5561-5568, 2018.","title":"A neural transition-based approach for semantic dependency graph parsing","authors":"Yuxuan Wang, Wanxiang Che, Jiang Guo, Ting Liu","year":"2018","venue":"Proceedings of AAAI","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Wang et al., 2019] Xinyu Wang, Jingxian Huang, and Kewei Tu. Second-order semantic dependency parsing with end-to-end neural networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4609-4618, July 2019.","title":"Second-order semantic dependency parsing with end-to-end neural networks","authors":"Xinyu Wang, Jingxian Huang, Kewei Tu","year":"2019","venue":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Xue et al., 2002] Nianwen Xue, Fu-Dong Chiou, and Martha Palmer. Building a large-scale annotated Chinese corpus. In COLING 2002: The 19th International Confer- ence on Computational Linguistics, 2002.","title":"Building a large-scale annotated Chinese corpus","authors":"Xue","year":"2002","venue":"COLING 2002: The 19th International Conference on Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2020] Yu Zhang, Zhenghua Li, and Min Zhang. Efficient second-order treecrf for neural depen- dency parsing. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault, editors, Proceedings of ACL, pages 3295-3305, 2020.","title":"Efficient second-order treecrf for neural dependency parsing","authors":"Yu Zhang, Zhenghua Li, Min Zhang","year":"2020","venue":"Proceedings of ACL","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhao et al., 2009] Hai Zhao, Wenliang Chen, Jun'ichi Kazama, Kiyotaka Uchimoto, and Kentaro Torisawa. Mul- tilingual dependency learning: Exploiting rich features for tagging syntactic and semantic dependencies. In Proceed- ings of CoNLL, pages 61-66, 2009.","title":"Multilingual dependency learning: Exploiting rich features for tagging syntactic and semantic dependencies","authors":"Hai Zhao, Wenliang Chen, Jun'ichi Kazama, Kiyotaka Uchimoto, Kentaro Torisawa","year":"2009","venue":"Proceedings of CoNLL","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhou and Zhao, 2019] Junru Zhou and Hai Zhao. Head- Driven Phrase Structure Grammar parsing on Penn Tree- bank. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2396- 2408, July 2019.","title":"Head-Driven Phrase Structure Grammar parsing on Penn Treebank","authors":"Zhao ; ] Junru Zhou, Hai Zhou, Zhao","year":"2019","venue":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}