[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/narrative-2016-kiperwasser-simple-accurate-dependency-parsing-bidirectional/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [English](https://md.mova.elamur.ai/free/en/docs/papers/en/) / Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations

# Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations

**Authors:** Eliyahu Kiperwasser, [Yoav Goldberg](https://md.mova.elamur.ai/free/en/docs/papers/authors/yoav-goldberg/) · **Year:** 2016 · **Venue:** Transactions of the Association for Computational Linguistics, vol. 4 (TACL) **Link:** [aclanthology.org/Q16-1023](https://aclanthology.org/Q16-1023/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**615** citations](https://openalex.org/works?filter=cites:W2301095666) openalex · 2026-10-18[**288** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/narrative-2016-kiperwasser-simple-accurate-dependency-parsing-bidirectional/) of the 140673 papers whose bibliographies we read[Transactions of the Association for Computational Linguistics](https://aclanthology.org/volumes/Q16-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/Q16-1023/)
- [PDF](https://aclanthology.org/Q16-1023.pdf)
- [DOI 10.1162/tacl_a_00101](https://doi.org/10.1162/tacl_a_00101)
- [arXiv 1603.04351](https://arxiv.org/abs/1603.04351)
- [OpenAlex](https://openalex.org/W2301095666)

**Authors (2)**

1. [Eliyahu Kiperwasser](https://openalex.org/A5006032775) h 6 · 831 citations · 9 works
2. [Yoav Goldberg](https://openalex.org/A5028476919) [ORCID](https://orcid.org/0000-0002-6497-829X) · h 63 · 24540 citations · 401 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper replaces hand-built feature templates with a BiLSTM over word and POS embeddings, trained jointly with the parser. A parser state is represented by the BiLSTM vectors of only a few items: in the greedy arc-hybrid transition parser, the top three stack items and the first buffer item (an extended set uses 11 vectors), scored by an MLP. The same encoder also feeds a first-order graph parser with loss-augmented training and a labeller that shares the encoder. Training uses a dynamic oracle, with aggressive exploration: a wrong action is sometimes followed when it scores within a margin of the right one (probability 0.1 in the experiments). Without pretrained embeddings the parsers reach 93.1 UAS on English Penn Treebank with Stanford dependencies and 86.6 UAS on Chinese, and with pretrained embeddings and the extended set they reach 93.9 and 87.6.

## Ideas

- I1. en/src/parse.rs: cut the perceptron's feature templates to the four positions this paper uses (s2, s1, s0, b0) and measure the LAS loss on EWT dev through `en parse-eval`; if the loss is small, the richer templates are not paying for themselves.
- I2. en/src/parse.rs: add aggressive exploration to training (follow a wrong action with probability 0.1 when it is within a margin of the right one) and measure LAS on EWT dev, twice, to confirm determinism.
- I3. gparse: word dropout with the paper's frequency rule (replace a word by the unknown symbol with probability alpha / (count + alpha), alpha = 0.25) and measure LAS on EWT dev and on ATIS dev, where lowercase queries give 63.0 LAS now.
- I4. gparse: a graph parser with a shared labeller head trained on the same encoder, compared with the current separate labelling step, measured by UAS on EWT dev.

## For Mova

The ideas touch the feature set of `en/src/parse.rs`, the training loop of `gparse`, and the ATIS evaluation. The chapters are `languages/en/20-syntax-overview` and `languages/en/26-questions` (ATIS queries are questions and commands). Measure first I1, which is an ablation of the current model and needs no new training code; then I2.

Topics: [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.1162/tacl_a_00101](https://doi.org/10.1162/tacl_a_00101) · [arXiv 1603.04351](https://arxiv.org/abs/1603.04351)

```
@article{kiperwasser2016simple,
  title = {{Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations}},
  author = {Kiperwasser, Eliyahu and Goldberg, Yoav},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2016},
  doi = {10.1162/tacl\_a\_00101},
  eprint = {1603.04351},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1162/tacl_a_00101}
}
```

- [← previous Algorithms for Deterministic Incremental Dependency Parsing](https://md.mova.elamur.ai/free/en/docs/papers/narrative-2008-nivre-algorithms-deterministic-incremental-dependency-parsing/)
- [next → Coreference meets Universal Dependencies – a pilot experiment on harmonizing coreference datasets for 11 languages](https://md.mova.elamur.ai/free/en/docs/papers/nedoluzhko-2021-coreference-meets-universal-dependencies/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

49 references, 10 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Miguel Ballesteros, Yoav Goldberg, Chris Dyer, and Noah A. Smith. 2016. Training with exploration improves a greedy stack-LSTM parser. CoRR, abs/1603.03793. [find](https://scholar.google.com/scholar?q=Miguel+Ballesteros%2C+Yoav+Goldberg%2C+Chris+Dyer%2C+and+Noah+A%2E+Smith%2E+2016%2E+Training+with+exploration+improves+a+greedy+stack%2DLSTM+parser%2E+CoRR%2C+abs%2F1603%2E03793%2E)
2. Rich Caruana. 1997. Multitask learning. Machine Learning, 28:41–75, July. [find](https://scholar.google.com/scholar?q=Rich+Caruana%2E+1997%2E+Multitask+learning%2E+Machine+Learning%2C+28%3A41%E2%80%9375%2C+July%2E)
3. Danqi Chen and Christopher Manning. 2014. A fast and accurate dependency parser using neural networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 740–750, Doha, Qatar, October. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Danqi+Chen+and+Christopher+Manning%2E+2014%2E+A+fast+and+accurate+dependency+parser+using+neural+networks%2E+In+Proceedings+of+the+2014+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29) [our card](https://md.mova.elamur.ai/free/en/docs/papers/missing-2014-chen-fast-accurate-dependency-parser-neural/)
4. Jason P.C. Chiu and Eric Nichols. 2016. Named entity recognition with bidirectional LSTM-CNNs. Transactions of the Association for Computational Linguistics, 4. To appear. [find](https://scholar.google.com/scholar?q=Jason+P%2EC%2E+Chiu+and+Eric+Nichols%2E+2016%2E+Named+entity+recognition+with+bidirectional+LSTM%2DCNNs%2E+Transactions+of+the+Association+for+Computational+Linguistics%2C+4%2E+To+appear%2E)
5. Kyunghyun Cho. 2015. Natural language understanding with distributed representation. CoRR, abs/1511.07916. — [arXiv:1511.07916](https://arxiv.org/abs/1511.07916) [find](https://scholar.google.com/scholar?q=Kyunghyun+Cho%2E+2015%2E+Natural+language+understanding+with+distributed+representation%2E+CoRR%2C+abs%2F1511%2E07916%2E+%E2%80%94+%5BarXiv%3A1511%2E07916%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1511%2E07916%29)
6. James Cross and Liang Huang. 2016. Incremental parsing with minimal features using bi-directional LSTM. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, August. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=James+Cross+and+Liang+Huang%2E+2016%2E+Incremental+parsing+with+minimal+features+using+bi%2Ddirectional+LSTM%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+Berl)
7. Marie-Catherine de Marneffe and Christopher D. Manning. 2008. Stanford dependencies manual. Technical report, Stanford University. [find](https://scholar.google.com/scholar?q=Marie%2DCatherine+de+Marneffe+and+Christopher+D%2E+Manning%2E+2008%2E+Stanford+dependencies+manual%2E+Technical+report%2C+Stanford+University%2E)
8. Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, and Noah A. Smith. 2015. Transition-based dependency parsing with stack long short-term memory. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural… [find](https://scholar.google.com/scholar?q=Chris+Dyer%2C+Miguel+Ballesteros%2C+Wang+Ling%2C+Austin+Matthews%2C+and+Noah+A%2E+Smith%2E+2015%2E+Transition%2Dbased+dependency+parsing+with+stack+long+short%2Dterm+memory%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of) [our card](https://md.mova.elamur.ai/free/en/docs/papers/missing-2015-dyer-transition-based-dependency-parsing-stack/)
9. Jason Eisner. 1996. Three new probabilistic models for dependency parsing: An exploration. In 16th International Conference on Computational Linguistics, Proceedings of the Conference, COLING 1996, Center for Sprogteknologi, Copenhagen, Denmark, August 5-9, 1996, pages 340–345. [find](https://scholar.google.com/scholar?q=Jason+Eisner%2E+1996%2E+Three+new+probabilistic+models+for+dependency+parsing%3A+An+exploration%2E+In+16th+International+Conference+on+Computational+Linguistics%2C+Proceedings+of+the+Conference%2C+COLING+1996%2C+Ce) [our card](https://md.mova.elamur.ai/free/en/docs/papers/ast-1996-eisner-three-new-probabilistic-models-dependency/)
10. Jeffrey L. Elman. 1990. Finding structure in time. Cognitive Science, 14(2):179–211. [find](https://scholar.google.com/scholar?q=Jeffrey+L%2E+Elman%2E+1990%2E+Finding+structure+in+time%2E+Cognitive+Science%2C+14%282%29%3A179%E2%80%93211%2E)
11. Yoav Goldberg and Joakim Nivre. 2012. A dynamic oracle for arc-eager dependency parsing. In Proceedings of COLING 2012, pages 959–976, Mumbai, India, December. [find](https://scholar.google.com/scholar?q=Yoav+Goldberg+and+Joakim+Nivre%2E+2012%2E+A+dynamic+oracle+for+arc%2Deager+dependency+parsing%2E+In+Proceedings+of+COLING+2012%2C+pages+959%E2%80%93976%2C+Mumbai%2C+India%2C+December%2E)
12. The COLING 2012 Organizing Committee. [find](https://scholar.google.com/scholar?q=The+COLING+2012+Organizing+Committee%2E)
13. Yoav Goldberg and Joakim Nivre. 2013. Training deterministic parsers with non-deterministic oracles. Transactions of the Association for Computational Linguistics, 1:403–414. [find](https://scholar.google.com/scholar?q=Yoav+Goldberg+and+Joakim+Nivre%2E+2013%2E+Training+deterministic+parsers+with+non%2Ddeterministic+oracles%2E+Transactions+of+the+Association+for+Computational+Linguistics%2C+1%3A403%E2%80%93414%2E)
14. Yoav Goldberg. 2015. A primer on neural network models for natural language processing. CoRR, abs/1510.00726. [find](https://scholar.google.com/scholar?q=Yoav+Goldberg%2E+2015%2E+A+primer+on+neural+network+models+for+natural+language+processing%2E+CoRR%2C+abs%2F1510%2E00726%2E)
15. Alex Graves. 2008. Supervised sequence labelling with recurrent neural networks. Ph.D. thesis, Technical University Munich. [find](https://scholar.google.com/scholar?q=Alex+Graves%2E+2008%2E+Supervised+sequence+labelling+with+recurrent+neural+networks%2E+Ph%2ED%2E+thesis%2C+Technical+University+Munich%2E)
16. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation, 9(8):1735– 1780. [find](https://scholar.google.com/scholar?q=Sepp+Hochreiter+and+J%C3%BCrgen+Schmidhuber%2E+1997%2E+Long+short%2Dterm+memory%2E+Neural+Computation%2C+9%288%29%3A1735%E2%80%93+1780%2E)
17. Liang Huang and Kenji Sagae. 2010. Dynamic programming for linear-time incremental parsing. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1077–1086, Uppsala, Sweden, July. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Liang+Huang+and+Kenji+Sagae%2E+2010%2E+Dynamic+programming+for+linear%2Dtime+incremental+parsing%2E+In+Proceedings+of+the+48th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+pages+1077%E2%80%931086%2C)
18. Ozan Irsoy and Claire Cardie. 2014. Opinion mining with deep recurrent neural networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 720–728, Doha, Qatar, October. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Ozan+Irsoy+and+Claire+Cardie%2E+2014%2E+Opinion+mining+with+deep+recurrent+neural+networks%2E+In+Proceedings+of+the+2014+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29%2C+pages+720%E2%80%93728)
19. Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III. 2015. Deep unordered composition rivals syntactic methods for text classification. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural La… [find](https://scholar.google.com/scholar?q=Mohit+Iyyer%2C+Varun+Manjunatha%2C+Jordan+Boyd%2DGraber%2C+and+Hal+Daum%C3%A9+III%2E+2015%2E+Deep+unordered+composition+rivals+syntactic+methods+for+text+classification%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+th)
20. Andrej Karpathy, Justin Johnson, and Fei-Fei Li. 2015. Visualizing and understanding recurrent networks. CoRR, abs/1506.02078. [find](https://scholar.google.com/scholar?q=Andrej+Karpathy%2C+Justin+Johnson%2C+and+Fei%2DFei+Li%2E+2015%2E+Visualizing+and+understanding+recurrent+networks%2E+CoRR%2C+abs%2F1506%2E02078%2E)
21. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference for Learning Representations, San Diego, California. [find](https://scholar.google.com/scholar?q=Diederik+P%2E+Kingma+and+Jimmy+Ba%2E+2015%2E+Adam%3A+A+method+for+stochastic+optimization%2E+In+Proceedings+of+the+3rd+International+Conference+for+Learning+Representations%2C+San+Diego%2C+California%2E)
22. Eliyahu Kiperwasser and Yoav Goldberg. 2016. Easy-first dependency parsing with hierarchical tree LSTMs. Transactions of the Association for Computational Linguistics, 4. To appear. [find](https://scholar.google.com/scholar?q=Eliyahu+Kiperwasser+and+Yoav+Goldberg%2E+2016%2E+Easy%2Dfirst+dependency+parsing+with+hierarchical+tree+LSTMs%2E+Transactions+of+the+Association+for+Computational+Linguistics%2C+4%2E+To+appear%2E)
23. Terry Koo and Michael Collins. 2010. Efficient third-order dependency parsers. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1–11, Uppsala, Sweden, July. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Terry+Koo+and+Michael+Collins%2E+2010%2E+Efficient+third%2Dorder+dependency+parsers%2E+In+Proceedings+of+the+48th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+pages+1%E2%80%9311%2C+Uppsala%2C+Sweden%2C+)
24. Terry Koo, Xavier Carreras, and Michael Collins. 2008. Simple semi-supervised dependency parsing. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, pages 595–603, Columbus, Ohio, June. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Terry+Koo%2C+Xavier+Carreras%2C+and+Michael+Collins%2E+2008%2E+Simple+semi%2Dsupervised+dependency+parsing%2E+In+Proceedings+of+the+46th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+pages+595%E2%80%93)
25. Sandra Kübler, Ryan T. McDonald, and Joakim Nivre. 2009. Dependency Parsing. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers. [find](https://scholar.google.com/scholar?q=Sandra+K%C3%BCbler%2C+Ryan+T%2E+McDonald%2C+and+Joakim+Nivre%2E+2009%2E+Dependency+Parsing%2E+Synthesis+Lectures+on+Human+Language+Technologies%2E+Morgan+%26+Claypool+Publishers%2E)
26. Marco Kuhlmann, Carlos Gómez-Rodríguez, and Giorgio Satta. 2011. Dynamic programming algorithms for transition-based dependency parsers. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 673–682, Portland, Oregon, USA, June… [find](https://scholar.google.com/scholar?q=Marco+Kuhlmann%2C+Carlos+G%C3%B3mez%2DRodr%C3%ADguez%2C+and+Giorgio+Satta%2E+2011%2E+Dynamic+programming+algorithms+for+transition%2Dbased+dependency+parsers%2E+In+Proceedings+of+the+49th+Annual+Meeting+of+the+Association+fo)
27. Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016. Neural architectures for named entity recognition. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, page… [find](https://scholar.google.com/scholar?q=Guillaume+Lample%2C+Miguel+Ballesteros%2C+Sandeep+Subramanian%2C+Kazuya+Kawakami%2C+and+Chris+Dyer%2E+2016%2E+Neural+architectures+for+named+entity+recognition%2E+In+Proceedings+of+the+2016+Conference+of+the+North+)
28. Phong Le and Willem Zuidema. 2014. The inside-outside recursive neural network model for dependency parsing. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 729–739, Doha, Qatar, October. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Phong+Le+and+Willem+Zuidema%2E+2014%2E+The+inside%2Doutside+recursive+neural+network+model+for+dependency+parsing%2E+In+Proceedings+of+the+2014+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28)
29. Yann LeCun, Sumit Chopra, Raia Hadsell, Marc’Aurelio Ranzato, and Fu Jie Huang. 2006. A tutorial on energy-based learning. Predicting structured data, 1. [find](https://scholar.google.com/scholar?q=Yann+LeCun%2C+Sumit+Chopra%2C+Raia+Hadsell%2C+Marc%E2%80%99Aurelio+Ranzato%2C+and+Fu+Jie+Huang%2E+2006%2E+A+tutorial+on+energy%2Dbased+learning%2E+Predicting+structured+data%2C+1%2E)
30. Tao Lei, Yu Xin, Yuan Zhang, Regina Barzilay, and Tommi Jaakkola. 2014. Low-rank tensors for scoring dependency structures. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1381–1391, Baltimore, Maryland, June. Association for… [find](https://scholar.google.com/scholar?q=Tao+Lei%2C+Yu+Xin%2C+Yuan+Zhang%2C+Regina+Barzilay%2C+and+Tommi+Jaakkola%2E+2014%2E+Low%2Drank+tensors+for+scoring+dependency+structures%2E+In+Proceedings+of+the+52nd+Annual+Meeting+of+the+Association+for+Computation)
31. Mike Lewis, Kenton Lee, and Luke Zettlemoyer. 2016. LSTM CCG parsing. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 221–231, San Diego, California, June. Association for Computational Linguisti… [find](https://scholar.google.com/scholar?q=Mike+Lewis%2C+Kenton+Lee%2C+and+Luke+Zettlemoyer%2E+2016%2E+LSTM+CCG+parsing%2E+In+Proceedings+of+the+2016+Conference+of+the+North+American+Chapter+of+the+Association+for+Computational+Linguistics%3A+Human+Langua) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2016-lewis-lstm-ccg-parsing/)
32. Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19(2):313–330. [find](https://scholar.google.com/scholar?q=Mitchell+P%2E+Marcus%2C+Beatrice+Santorini%2C+and+Mary+Ann+Marcinkiewicz%2E+1993%2E+Building+a+large+annotated+corpus+of+English%3A+The+Penn+Treebank%2E+Computational+Linguistics%2C+19%282%29%3A313%E2%80%93330%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/bank-1993-marcus-building-large-annotated-corpus-english/)
33. Andre Martins, Noah A. Smith, and Eric Xing. 2009. Concise integer linear programming formulations for dependency parsing. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pages 342–35… [find](https://scholar.google.com/scholar?q=Andre+Martins%2C+Noah+A%2E+Smith%2C+and+Eric+Xing%2E+2009%2E+Concise+integer+linear+programming+formulations+for+dependency+parsing%2E+In+Proceedings+of+the+Joint+Conference+of+the+47th+Annual+Meeting+of+the+ACL+)
34. Andre Martins, Miguel Almeida, and Noah A. Smith. 2013. Turning on the turbo: Fast third-order non-projective turbo parsers. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 617–622, Sofia, Bulgaria, August. Association for Co… [find](https://scholar.google.com/scholar?q=Andre+Martins%2C+Miguel+Almeida%2C+and+Noah+A%2E+Smith%2E+2013%2E+Turning+on+the+turbo%3A+Fast+third%2Dorder+non%2Dprojective+turbo+parsers%2E+In+Proceedings+of+the+51st+Annual+Meeting+of+the+Association+for+Computatio) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2013-martins-turning-turbo-fast-third-order/)
35. Ryan McDonald, Koby Crammer, and Fernando Pereira. 2005. Online large-margin training of dependency parsers. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), pages 91–98, Ann Arbor, Michigan, June. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Ryan+McDonald%2C+Koby+Crammer%2C+and+Fernando+Pereira%2E+2005%2E+Online+large%2Dmargin+training+of+dependency+parsers%2E+In+Proceedings+of+the+43rd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+) [our card](https://md.mova.elamur.ai/free/en/docs/papers/narrative-2005-mcdonald-online-large-margin-training-dependency/)
36. Ryan McDonald. 2006. Discriminative Training and Spanning Tree Algorithms for Dependency Parsing. Ph.D. thesis, University of Pennsylvania. [find](https://scholar.google.com/scholar?q=Ryan+McDonald%2E+2006%2E+Discriminative+Training+and+Spanning+Tree+Algorithms+for+Dependency+Parsing%2E+Ph%2ED%2E+thesis%2C+University+of+Pennsylvania%2E)
37. Joakim Nivre. 2004. Incrementality in deterministic dependency parsing. In Frank Keller, Stephen Clark, Matthew Crocker, and Mark Steedman, editors, Proceedings of the ACL Workshop Incremental Parsing: Bringing Engineering and Cognition Together, pages 50–57, Barcelona, Spain, July. Association for… [find](https://scholar.google.com/scholar?q=Joakim+Nivre%2E+2004%2E+Incrementality+in+deterministic+dependency+parsing%2E+In+Frank+Keller%2C+Stephen+Clark%2C+Matthew+Crocker%2C+and+Mark+Steedman%2C+editors%2C+Proceedings+of+the+ACL+Workshop+Incremental+Parsing) [our card](https://md.mova.elamur.ai/free/en/docs/papers/nivre-2004-incrementality-deterministic-dependency-parsing/)
38. Joakim Nivre. 2008. Algorithms for deterministic incremental dependency parsing. Computational Linguistics, 34(4):513–553. [find](https://scholar.google.com/scholar?q=Joakim+Nivre%2E+2008%2E+Algorithms+for+deterministic+incremental+dependency+parsing%2E+Computational+Linguistics%2C+34%284%29%3A513%E2%80%93553%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/narrative-2008-nivre-algorithms-deterministic-incremental-dependency-parsing/)
39. Wenzhe Pei, Tao Ge, and Baobao Chang. 2015. An effective neural network model for graph-based dependency parsing. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Paper… [find](https://scholar.google.com/scholar?q=Wenzhe+Pei%2C+Tao+Ge%2C+and+Baobao+Chang%2E+2015%2E+An+effective+neural+network+model+for+graph%2Dbased+dependency+parsing%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+Association+for+Computational+Linguis)
40. Mike Schuster and Kuldip K. Paliwal. 1997. Bidirectional recurrent neural networks. IEEE Trans. Signal Processing, 45(11):2673–2681. [find](https://scholar.google.com/scholar?q=Mike+Schuster+and+Kuldip+K%2E+Paliwal%2E+1997%2E+Bidirectional+recurrent+neural+networks%2E+IEEE+Trans%2E+Signal+Processing%2C+45%2811%29%3A2673%E2%80%932681%2E)
41. Benjamin Taskar, Vassil Chatalbashev, Daphne Koller, and Carlos Guestrin. 2005. Learning structured prediction models: A large margin approach. In Machine Learning, Proceedings of the Twenty-Second International Conference (ICML 2005), Bonn, Germany, August 7-11, 2005, pages 896–903. [find](https://scholar.google.com/scholar?q=Benjamin+Taskar%2C+Vassil+Chatalbashev%2C+Daphne+Koller%2C+and+Carlos+Guestrin%2E+2005%2E+Learning+structured+prediction+models%3A+A+large+margin+approach%2E+In+Machine+Learning%2C+Proceedings+of+the+Twenty%2DSecond+In)
42. Hillel Taub-Tabib, Yoav Goldberg, and Amir Globerson. 2015. Template kernels for dependency parsing. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1422–1427, Denver, Colorado, May–June. Associa… [find](https://scholar.google.com/scholar?q=Hillel+Taub%2DTabib%2C+Yoav+Goldberg%2C+and+Amir+Globerson%2E+2015%2E+Template+kernels+for+dependency+parsing%2E+In+Proceedings+of+the+2015+Conference+of+the+North+American+Chapter+of+the+Association+for+Computat)
43. Ivan Titov and James Henderson. 2007. A latent variable model for generative dependency parsing. In Proceedings of the Tenth International Conference on Parsing Technologies, pages 144–155, Prague, Czech Republic, June. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Ivan+Titov+and+James+Henderson%2E+2007%2E+A+latent+variable+model+for+generative+dependency+parsing%2E+In+Proceedings+of+the+Tenth+International+Conference+on+Parsing+Technologies%2C+pages+144%E2%80%93155%2C+Prague%2C+Cz)
44. Ashish Vaswani, Yonatan Bisk, Kenji Sagae, and Ryan Musa. 2016. Supertagging with LSTMs. In Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics (Short Papers), San Diego, California, June. [find](https://scholar.google.com/scholar?q=Ashish+Vaswani%2C+Yonatan+Bisk%2C+Kenji+Sagae%2C+and+Ryan+Musa%2E+2016%2E+Supertagging+with+LSTMs%2E+In+Proceedings+of+the+15th+Annual+Conference+of+the+North+American+Chapter+of+the+Association+for+Computational)
45. Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, and Geoffrey E. Hinton. 2015. Grammar as a foreign language. In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada,… [find](https://scholar.google.com/scholar?q=Oriol+Vinyals%2C+Lukasz+Kaiser%2C+Terry+Koo%2C+Slav+Petrov%2C+Ilya+Sutskever%2C+and+Geoffrey+E%2E+Hinton%2E+2015%2E+Grammar+as+a+foreign+language%2E+In+Advances+in+Neural+Information+Processing+Systems+28%3A+Annual+Confe)
46. David Weiss, Chris Alberti, Michael Collins, and Slav Petrov. 2015. Structured training for neural network transition-based parsing. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (V… [find](https://scholar.google.com/scholar?q=David+Weiss%2C+Chris+Alberti%2C+Michael+Collins%2C+and+Slav+Petrov%2E+2015%2E+Structured+training+for+neural+network+transition%2Dbased+parsing%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+Association+for+Co)
47. Yue Zhang and Stephen Clark. 2008. A tale of two parsers: Investigating and combining graph-based and transition-based dependency parsing. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 562–571, Honolulu, Hawaii, October. Association for Computationa… [find](https://scholar.google.com/scholar?q=Yue+Zhang+and+Stephen+Clark%2E+2008%2E+A+tale+of+two+parsers%3A+Investigating+and+combining+graph%2Dbased+and+transition%2Dbased+dependency+parsing%2E+In+Proceedings+of+the+2008+Conference+on+Empirical+Methods+in) [our card](https://md.mova.elamur.ai/free/en/docs/papers/zhang-2008-tale-two-parsers-investigating-combining/)
48. Yue Zhang and Joakim Nivre. 2011. Transition-based dependency parsing with rich non-local features. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 188–193, Portland, Oregon, USA, June. Association for Computational Lingu… [find](https://scholar.google.com/scholar?q=Yue+Zhang+and+Joakim+Nivre%2E+2011%2E+Transition%2Dbased+dependency+parsing+with+rich+non%2Dlocal+features%2E+In+Proceedings+of+the+49th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%3A+Human+La)
49. Chenxi Zhu, Xipeng Qiu, Xinchi Chen, and Xuanjing Huang. 2015. A re-ranking model for dependency parser with recursive convolutional neural network. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Langua… [find](https://scholar.google.com/scholar?q=Chenxi+Zhu%2C+Xipeng+Qiu%2C+Xinchi+Chen%2C+and+Xuanjing+Huang%2E+2015%2E+A+re%2Dranking+model+for+dependency+parser+with+recursive+convolutional+neural+network%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+As)