References#
Correia G., Niculae V., Aziz W., & Martins A. Efficient marginalization of discrete and structured latent variables via sparsity. In Advances in NIPS, 11789–11802 (2020).
Devlin J., Chang M., Lee K., & Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL, 4171–4186 (2019).
Dozat T. & Manning C. Deep biaffine attention for neural dependency parsing. In Proceedings of ICLR, (2017).
Dozat T. & Manning C. Simpler but more accurate semantic dependency parsing. In Proceedings of ACL, 484–490 (2018).
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 J. Inside-outside and forward-backward algorithms are just backprop (tutorial paper). In Proceedings of WS, 1–17 (2016).
Eisner J. & Satta G. Efficient parsing for bilexical context-free grammars and head automaton grammars. In Proceedings of ACL, 457–464 (1999).
Gal Y. & Ghahramani Z. Dropout as a bayesian approximation: representing model uncertainty in deep learning. In Proceedings of ICML, 1050–1059 (2016).
Goodman J. Semiring parsing. Computational Linguistics, 573–606 (1999).
Hwa R. Sample selection for statistical grammar induction. In Proceedings of ACL, 45–52 (2000).
Kim Y., Rush A., Yu L., Kuncoro A., Dyer C., et al. Unsupervised recurrent neural network grammars. In Proceedings of NAACL, 1105–1117 (2019).
Kitaev N. & Klein D. Tetra-tagging: word-synchronous parsing with linear-time inference. In Proceedings of ACL, 6255–6261 (2020).
Koo T., Globerson A., Carreras X., & Collins M. Structured prediction models via the matrix-tree theorem. In Proceedings of EMNLP, 141–150 (2007).
Lafferty J., McCallum A., & Pereira F. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In Proceedings of ICML, 282–289 (2001).
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 (2009).
Ma X. & Hovy E. Neural probabilistic model for non-projective MST parsing. In Proceedings of IJCNLP, 59–69 (2017).
Martins A. & Astudillo R. From softmax to sparsemax: a sparse model of attention and multi-label classification. In Proceedings of ICML, 1614–1623 (2016).
McDonald R. & Pereira F. Online learning of approximate dependency parsing algorithms. In Proceedings of EACL, 81–88 (2006).
McDonald R., Pereira F., Ribarov K., & Haji\vc J. Non-projective dependency parsing using spanning tree algorithms. In Proceedings of EMNLP, 523–530 (2005).
Mensch A. & Blondel M. Differentiable dynamic programming for structured prediction and attention. In Proceedings of ICML, 3462–3471 (2018).
Peters M., Neumann M., Iyyer M., Gardner M., Clark C., et al. Deep contextualized word representations. In Proceedings of NAACL, 2227–2237 (2018).
Sarawagi S. & Cohen W. Semi-markov conditional random fields for information extraction. In Advances in NIPS, 1185–1192 (2004).
Smith D. & Eisner J. Dependency parsing by belief propagation. In Proceedings of EMNLP, 145–156 (2008).
Stern M., Andreas J., & Klein D. A minimal span-based neural constituency parser. In Proceedings of ACL, 818–827 (2017).
Wang X., Huang J., & Tu K. Second-order semantic dependency parsing with end-to-end neural networks. In Proceedings of ACL, 4609–4618 (2019).
Wang X. & Tu K. Second-order neural dependency parsing with message passing and end-to-end training. In Proceedings of AACL, 93–99 (2020).
Yang K. & Deng J. Strongly incremental constituency parsing with graph neural networks. In Advances in NIPS, 21687–21698 (2020).
Yang S., Zhao Y., & Tu K. Neural bi-lexicalized PCFG induction. In Proceedings of ACL, 2688–2699 (2021).
Zhang Y., Li Z., & Zhang M. Efficient second-order TreeCRF for neural dependency parsing. In Proceedings of ACL, 3295–3305 (2020a).
Zhang Y., Zhou h., & Li Z. Fast and accurate neural crf constituency parsing. In Proceedings of IJCAI, 4046–4053 (2020b).