References#

Buchholz & Marsi 2006

Buchholz S. & Marsi E. CoNLL-X shared task on multilingual dependency parsing. In Proceedings of CoNLL, 149–164. New York City, 2006. Association for Computational Linguistics. URL: https://aclanthology.org/W06-2920.

Correia et al. 2020

Correia G., Niculae V., Aziz W., & Martins A. Efficient marginalization of discrete and structured latent variables via sparsity. In Advances in NIPS, 11789–11802. Curran Associates, Inc., 2020. URL: https://proceedings.neurips.cc/paper/2020/hash/887caadc3642e304ede659b734f79b00-Abstract.html.

Devlin et al. 2019

Devlin J., Chang M., Lee K., & Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL, 4171–4186. Minneapolis, Minnesota, 2019. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/N19-1423.

Dozat & Manning 2017

Dozat T. & Manning C. Deep biaffine attention for neural dependency parsing. In Proceedings of ICLR. Toulon, France, 2017. OpenReview.net. URL: https://openreview.net/forum?id=Hk95PK9le.

Dozat & Manning 2018

Dozat T. & Manning C. Simpler but more accurate semantic dependency parsing. In Proceedings of ACL, 484–490. Melbourne, Australia, 2018. Association for Computational Linguistics. URL: https://aclanthology.org/P18-2077.

Eisner 2000

Eisner J. Bilexical Grammars and their Cubic-Time Parsing Algorithms, pages 29–61. Springer Netherlands, Dordrecht, 2000. URL: https://www.cs.jhu.edu/~jason/papers/eisner.iwptbook00.pdf.

Eisner 2016

Eisner J. Inside-outside and forward-backward algorithms are just backprop (tutorial paper). In Proceedings of WS, 1–17. Austin, TX, 2016. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/W16-5901.

Eisner & Satta 1999

Eisner J. & Satta G. Efficient parsing for bilexical context-free grammars and head automaton grammars. In Proceedings of ACL, 457–464. Association for Computational Linguistics, 1999. URL: https://aclanthology.org/P99-1059.

Gal & Ghahramani 2016

Gal Y. & Ghahramani Z. Dropout as a bayesian approximation: representing model uncertainty in deep learning. In Proceedings of ICML, 1050–1059. New York, New York, USA, 2016. PMLR. URL: http://proceedings.mlr.press/v48/gal16.html.

Goodman 1999

Goodman J. Semiring parsing. Computational Linguistics, 573–606 (1999).

Hwa 2000

Hwa R. Sample selection for statistical grammar induction. In Proceedings of ACL, 45–52. Hong Kong, China, 2000. Association for Computational Linguistics. URL: https://aclanthology.org/W00-1306, doi:10.3115/1117794.1117800.

Kim et al. 2019

Kim Y., Rush A., Yu L., Kuncoro A., Dyer C., et al. Unsupervised recurrent neural network grammars. In Proceedings of NAACL, 1105–1117. Minneapolis, Minnesota, 2019. Association for Computational Linguistics. URL: https://aclanthology.org/N19-1114.

Koo et al. 2007

Koo T., Globerson A., Carreras X., & Collins M. Structured prediction models via the matrix-tree theorem. In Proceedings of EMNLP, 141–150. Prague, Czech Republic, 2007. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/D07-1015.

Lafferty et al. 2001

Lafferty J., McCallum A., & Pereira F. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In Proceedings of ICML, 282–289. Williams College, Williamstown, MA, USA, 2001. Morgan Kaufmann. URL: http://www.aladdin.cs.cmu.edu/papers/pdfs/y2001/crf.pdf.

Li & Eisner 2009

Li Z. & Eisner J. First- and second-order expectation semirings with applications to minimum-risk training on translation forests. In Proceedings of EMNLP, 40–51. Singapore, 2009. Association for Computational Linguistics. URL: https://aclanthology.org/D09-1005.

Ma & Hovy 2017

Ma X. & Hovy E. Neural probabilistic model for non-projective MST parsing. In Proceedings of IJCNLP, 59–69. Taipei, Taiwan, 2017. Asian Federation of Natural Language Processing. URL: https://aclanthology.org/I17-1007.

Martins & Astudillo 2016

Martins A. & Astudillo R. From softmax to sparsemax: a sparse model of attention and multi-label classification. In Proceedings of ICML, 1614–1623. New York, New York, USA, 2016. PMLR. URL: https://proceedings.mlr.press/v48/martins16.html.

McDonald & Pereira 2006

McDonald R. & Pereira F. Online learning of approximate dependency parsing algorithms. In Proceedings of EACL, 81–88. Trento, Italy, 2006. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/E06-1011.

McDonald et al. 2005

McDonald R., Pereira F., Ribarov K., & Haji\vc J. Non-projective dependency parsing using spanning tree algorithms. In Proceedings of EMNLP, 523–530. Vancouver, British Columbia, Canada, 2005. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/H05-1066.

Mensch & Blondel 2018

Mensch A. & Blondel M. Differentiable dynamic programming for structured prediction and attention. In Proceedings of ICML, 3462–3471. PMLR, 2018. URL: https://proceedings.mlr.press/v80/mensch18a.html.

Peters et al. 2018

Peters M., Neumann M., Iyyer M., Gardner M., Clark C., et al. Deep contextualized word representations. In Proceedings of NAACL, 2227–2237. New Orleans, Louisiana, 2018. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/W18-6202.

Smith & Eisner 2008

Smith D. & Eisner J. Dependency parsing by belief propagation. In Proceedings of EMNLP, 145–156. Honolulu, Hawaii, 2008. Association for Computational Linguistics. URL: https://aclanthology.org/D08-1016.

Stern et al. 2017

Stern M., Andreas J., & Klein D. A minimal span-based neural constituency parser. In Proceedings of ACL, 818–827. Vancouver, Canada, 2017. Association for Computational Linguistics. URL: https://aclanthology.org/P17-1076.

Wang et al. 2019

Wang X., Huang J., & Tu K. Second-order semantic dependency parsing with end-to-end neural networks. In Proceedings of ACL, 4609–4618. Florence, Italy, 2019. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/P19-1454.

Wang & Tu 2020

Wang X. & Tu K. Second-order neural dependency parsing with message passing and end-to-end training. In Proceedings of AACL, 93–99. Suzhou, China, 2020. Association for Computational Linguistics. URL: https://aclanthology.org/2020.aacl-main.12.

Yang & Deng 2020

Yang K. & Deng J. Strongly incremental constituency parsing with graph neural networks. In Advances in NIPS, 21687–21698. Curran Associates, Inc., 2020. URL: https://papers.nips.cc/paper/2020/hash/f7177163c833dff4b38fc8d2872f1ec6-Abstract.html.

Yang et al. 2021

Yang S., Zhao Y., & Tu K. Neural bi-lexicalized PCFG induction. In Proceedings of ACL, 2688–2699. Association for Computational Linguistics, 2021. URL: https://aclanthology.org/2021.acl-long.209.

Zhang et al. 2020a

Zhang Y., Li Z., & Zhang M. Efficient second-order TreeCRF for neural dependency parsing. In Proceedings of ACL, 3295–3305. Online, 2020a. Association for Computational Linguistics. URL: https://aclanthology.org/2020.acl-main.302.

Zhang et al. 2020b

Zhang Y., Zhou h., & Li Z. Fast and accurate neural crf constituency parsing. In Proceedings of IJCAI, 4046–4053. Online, 2020b. International Joint Conferences on Artificial Intelligence Organization. URL: https://www.ijcai.org/Proceedings/2020/560/.