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# The Galactic Dependencies Treebanks: Getting More Data by Synthesizing New Languages

**Authors:** Dingquan Wang and [Jason Eisner](https://md.mova.elamur.ai/free/en/docs/papers/authors/jason-eisner/) · **Year:** 2016 · **Venue:** Transactions of the Association for Computational Linguistics (TACL) **Link:** [aclanthology.org/Q16-1035](https://aclanthology.org/Q16-1035/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**58** citations](https://openalex.org/works?filter=cites:W2963737810) openalex · 2026-10-18[**40** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/ud-2016-wang-galactic-dependencies-treebanks-getting-more/) 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/Q16-1035/)
- [PDF](https://aclanthology.org/Q16-1035.pdf)
- [DOI 10.1162/tacl_a_00113](https://doi.org/10.1162/tacl_a_00113)
- [arXiv 1710.03838](https://arxiv.org/abs/1710.03838)
- [OpenAlex](https://openalex.org/W2963737810)

**Authors (2)**

1. [Dingquan Wang](https://openalex.org/A5043674345) [ORCID](https://orcid.org/0009-0008-8783-912X) · h 9 · 259 citations · 36 works
2. [Jason Eisner](https://openalex.org/A5052467896) [ORCID](https://orcid.org/0000-0002-8861-0772) · h 51 · 8141 citations · 307 works

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

## What it is about

Galactic Dependencies 1.0 is a set of synthetic languages in UD format. Each is made from a real UD treebank by permuting the order of the dependents of nouns and verbs, to match the word order of other real languages. Starting from 37 real treebanks, the set gives 53,428 languages in total. The authors check realism by parsability and by the perplexity of POS sequences. In single-source transfer, choosing the source from a pool of 1,210 options (m) instead of 10 real languages raises average target UAS by 2.3 points with supervised selection, and by 1.2 points with unsupervised selection. The difference is significant over 17 targets for supervised selection, and the main experiments use gold POS tags.

## Ideas

- I1. Word-order stress test (test only): reorder noun and verb dependents in the EWT test set and in the GUM test set, and measure `en parse-eval` LAS on each version against the original. This tests how much the parser depends on the usual order.
- I2. Transfer check: train the `en` perceptron on permuted English and test on EWT dev; compare LAS with the unpermuted baseline, with two identical runs.
- I3. Realism check: compute the POS-trigram perplexity of each permuted set with the trigram model of `en/src/tag.rs`, as the paper does with its synthetic sets, and drop permutations that look unlike English.

## For Mova

The code is `en/src/parse.rs`, and the treebank habits are under `treebanks/en/ewt`. Permuted EWT may be used for training, since EWT is CC BY-SA, but keep the set small and cite its source. Permuted GUM is test only, because GUM is NC. The paper's transfer numbers are for other languages.

Topics: [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.1162/tacl_a_00113](https://doi.org/10.1162/tacl_a_00113) · [arXiv 1710.03838](https://arxiv.org/abs/1710.03838)

```
@article{wang2016galactic,
  title = {{The Galactic Dependencies Treebanks: Getting More Data by Synthesizing New Languages}},
  author = {Wang, Dingquan and Eisner, Jason},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2016},
  doi = {10.1162/tacl\_a\_00113},
  eprint = {1710.03838},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1162/tacl_a_00113}
}
```

- [← previous UDPipe: Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization, Morphological Analysis, POS Tagging and Parsing](https://md.mova.elamur.ai/free/en/docs/papers/ud-2016-straka-udpipe-trainable-pipeline-processing-conll/)
- [next → 'IMS at the CoNLL 2017 UD Shared Task: CRFs and Perceptrons Meet Neural Networks'](https://md.mova.elamur.ai/free/en/docs/papers/ud-2017-bjorkelund-ims-conll-2017-ud-shared/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

41 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, and Ting Liu. Cross-lingual dependency parsing based on distributed representations. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Proces… [find](https://scholar.google.com/scholar?q=Jiang+Guo%2C+Wanxiang+Che%2C+David+Yarowsky%2C+Haifeng+Wang%2C+and+Ting+Liu%2E+Cross%2Dlingual+dependency+parsing+based+on+distributed+representations%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+Association)
2. Yaser S. Abu-Mostafa. Hints. Neural Computation, 7: 639–671, July 1995. [find](https://scholar.google.com/scholar?q=Yaser+S%2E+Abu%2DMostafa%2E+Hints%2E+Neural+Computation%2C+7%3A+639%E2%80%93671%2C+July+1995%2E)
3. Ingwer Borg and Patrick J.F. Groenen. Modern Multidimensional Scaling: Theory and Applications. 2005. [find](https://scholar.google.com/scholar?q=Ingwer+Borg+and+Patrick+J%2EF%2E+Groenen%2E+Modern+Multidimensional+Scaling%3A+Theory+and+Applications%2E+2005%2E)
4. Noam Chomsky. Lectures on Government and Binding: The Pisa Lectures. Holland: Foris Publications, 1981. [find](https://scholar.google.com/scholar?q=Noam+Chomsky%2E+Lectures+on+Government+and+Binding%3A+The+Pisa+Lectures%2E+Holland%3A+Foris+Publications%2C+1981%2E)
5. Jason Eisner and Noah A. Smith. Favor short dependencies: Parsing with soft and hard constraints on dependency length. In Harry Bunt, Paola Merlo, and Joakim Nivre, editors, Trends in Parsing Technology: Dependency Parsing, Domain Adaptation, and Deep Parsing, chapter 8, pages 121–150. 2010. [find](https://scholar.google.com/scholar?q=Jason+Eisner+and+Noah+A%2E+Smith%2E+Favor+short+dependencies%3A+Parsing+with+soft+and+hard+constraints+on+dependency+length%2E+In+Harry+Bunt%2C+Paola+Merlo%2C+and+Joakim+Nivre%2C+editors%2C+Trends+in+Parsing+Technolo)
6. Kuzman Ganchev, Joao Graça, Jennifer Gillenwater, and Ben Taskar. Posterior regularization for structured latent variable models. Journal of Machine Learning Research, 11:2001–2049, 2010. [find](https://scholar.google.com/scholar?q=Kuzman+Ganchev%2C+Joao+Grac%CC%A7a%2C+Jennifer+Gillenwater%2C+and+Ben+Taskar%2E+Posterior+regularization+for+structured+latent+variable+models%2E+Journal+of+Machine+Learning+Research%2C+11%3A2001%E2%80%932049%2C+2010%2E)
7. John Hawkins. A Performance Theory of Order and Constituency. Cambridge University Press, 1994. [find](https://scholar.google.com/scholar?q=John+Hawkins%2E+A+Performance+Theory+of+Order+and+Constituency%2E+Cambridge+University+Press%2C+1994%2E)
8. Michael Collins, Philipp Koehn, and Ivona Kucerova. Clause restructuring for statistical machine translation. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pages 531– 540, 2005. [find](https://scholar.google.com/scholar?q=Michael+Collins%2C+Philipp+Koehn%2C+and+Ivona+Kucerova%2E+Clause+restructuring+for+statistical+machine+translation%2E+In+Proceedings+of+the+43rd+Annual+Meeting+of+the+Association+for+Computational+Linguistics)
9. Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. Teaching machines to read and comprehend. In Advances in Neural Information Processing Systems, pages 1684–1692, 2015. [find](https://scholar.google.com/scholar?q=Karl+Moritz+Hermann%2C+Tomas+Kocisky%2C+Edward+Grefenstette%2C+Lasse+Espeholt%2C+Will+Kay%2C+Mustafa+Suleyman%2C+and+Phil+Blunsom%2E+Teaching+machines+to+read+and+comprehend%2E+In+Advances+in+Neural+Information+Proce)
10. Ryan Cotterell, Nanyun Peng, and Jason Eisner. Modeling word forms using latent underlying morphs and phonology. Transactions of the Association for Computational Linguistics, 3:433–447, 2015. [find](https://scholar.google.com/scholar?q=Ryan+Cotterell%2C+Nanyun+Peng%2C+and+Jason+Eisner%2E+Modeling+word+forms+using+latent+underlying+morphs+and+phonology%2E+Transactions+of+the+Association+for+Computational+Linguistics%2C+3%3A433%E2%80%93447%2C+2015%2E)
11. Susan Howlett and Mark Dras. Clause restructuring for SMT not absolutely helpful. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 384–388, 2011. Erratum at [https://www.aclweb.org/anthology/P/](https://www.aclweb.org/anthology/P/) P11/P11-2067e1.pdf. [↗](https://www.aclweb.org/anthology/P/)
12. Xiaodong Cui, Vaibhava Goel, and Brian Kingsbury. Data augmentation for deep neural network acoustic modeling. IEEE/ACM Transactions on Audio, Speech and Language Processing, 23(9):1469–1477, September 2015. ISSN 2329-9290. [find](https://scholar.google.com/scholar?q=Xiaodong+Cui%2C+Vaibhava+Goel%2C+and+Brian+Kingsbury%2E+Data+augmentation+for+deep+neural+network+acoustic+modeling%2E+IEEE%2FACM+Transactions+on+Audio%2C+Speech+and+Language+Processing%2C+23%289%29%3A1469%E2%80%931477%2C+Septembe)
13. Bonnie J. Dorr. Machine Translation: A View from the Lexicon. MIT Press, Cambridge, MA, 1993. [find](https://scholar.google.com/scholar?q=Bonnie+J%2E+Dorr%2E+Machine+Translation%3A+A+View+from+the+Lexicon%2E+MIT+Press%2C+Cambridge%2C+MA%2C+1993%2E)
14. Bonnie J. Dorr, Lisa Pearl, Rebecca Hwa, and Nizar Habash. DUSTer: A method for unraveling cross-language divergences for statistical word-level alignment. In Proceedings of the 5th Conference of the Association for Machine Translation in the Americas on Machine Translation: From Research to Real Us… [find](https://scholar.google.com/scholar?q=Bonnie+J%2E+Dorr%2C+Lisa+Pearl%2C+Rebecca+Hwa%2C+and+Nizar+Habash%2E+DUSTer%3A+A+method+for+unraveling+cross%2Dlanguage+divergences+for+statistical+word%2Dlevel+alignment%2E+In+Proceedings+of+the+5th+Conference+of+the+)
15. Matthew S. Dryer and Martin Haspelmath, editors. The World Atlas of Language Structures Online. Max Planck Institute for Evolutionary Anthropology, Leipzig, 2013. [find](https://scholar.google.com/scholar?q=Matthew+S%2E+Dryer+and+Martin+Haspelmath%2C+editors%2E+The+World+Atlas+of+Language+Structures+Online%2E+Max+Planck+Institute+for+Evolutionary+Anthropology%2C+Leipzig%2C+2013%2E)
16. Navdeep Jaitly and Geoffrey E. Hinton. Vocal tract length perturbation (VTLP) improves speech recognition. In Proceedings of the 30th International Conference on Machine Learning Workshop on Deep Learning for Audio, Speech and Language, 2013. [find](https://scholar.google.com/scholar?q=Navdeep+Jaitly+and+Geoffrey+E%2E+Hinton%2E+Vocal+tract+length+perturbation+%28VTLP%29+improves+speech+recognition%2E+In+Proceedings+of+the+30th+International+Conference+on+Machine+Learning+Workshop+on+Deep+Lear)
17. Dan Klein and Christopher Manning. Corpus-based induction of syntactic structure: Models of dependency and constituency. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, pages 478–485, 2004. [find](https://scholar.google.com/scholar?q=Dan+Klein+and+Christopher+Manning%2E+Corpus%2Dbased+induction+of+syntactic+structure%3A+Models+of+dependency+and+constituency%2E+In+Proceedings+of+the+42nd+Annual+Meeting+of+the+Association+for+Computational+)
18. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. ImageNet classification with deep convolutional neural networks. In F. Pereira, C.J.C. Burges, L. Bottou, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 1097–1105. 2012. [find](https://scholar.google.com/scholar?q=Alex+Krizhevsky%2C+Ilya+Sutskever%2C+and+Geoffrey+E%2E+Hinton%2E+ImageNet+classification+with+deep+convolutional+neural+networks%2E+In+F%2E+Pereira%2C+C%2EJ%2EC%2E+Burges%2C+L%2E+Bottou%2C+and+K%2EQ%2E+Weinberger%2C+editors%2C+Advance)
19. Xuezhe Ma and Fei Xia. Unsupervised dependency parsing with transferring distribution via parallel guidance and entropy regularization. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, volume 1, pages 1337–1348, 2014. [find](https://scholar.google.com/scholar?q=Xuezhe+Ma+and+Fei+Xia%2E+Unsupervised+dependency+parsing+with+transferring+distribution+via+parallel+guidance+and+entropy+regularization%2E+In+Proceedings+of+the+52nd+Annual+Meeting+of+the+Association+for)
20. Martin Majliš. W2C—web to corpus—corpora, 2011. LINDAT/CLARIN digital library at the Institute of Formal and Applied Linguistics, Charles University in Prague. [find](https://scholar.google.com/scholar?q=Martin+Majlis%CC%8C%2E+W2C%E2%80%94web+to+corpus%E2%80%94corpora%2C+2011%2E+LINDAT%2FCLARIN+digital+library+at+the+Institute+of+Formal+and+Applied+Linguistics%2C+Charles+University+in+Prague%2E)
21. Ryan McDonald, Slav Petrov, and Keith Hall. Multi-source transfer of delexicalized dependency parsers. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 62– 72, 2011. [find](https://scholar.google.com/scholar?q=Ryan+McDonald%2C+Slav+Petrov%2C+and+Keith+Hall%2E+Multi%2Dsource+transfer+of+delexicalized+dependency+parsers%2E+In+Proceedings+of+the+2011+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+)
22. Tahira Naseem, Harr Chen, Regina Barzilay, and Mark Johnson. Using universal linguistic knowledge to guide grammar induction. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 1234–1244, 2010. [find](https://scholar.google.com/scholar?q=Tahira+Naseem%2C+Harr+Chen%2C+Regina+Barzilay%2C+and+Mark+Johnson%2E+Using+universal+linguistic+knowledge+to+guide+grammar+induction%2E+In+Proceedings+of+the+2010+Conference+on+Empirical+Methods+in+Natural+Lang)
23. Dat Quoc Nguyen, Dai Quoc Nguyen, Dang Duc Pham, and Son Bao Pham. RDRPOSTagger: A ripple down rules-based part-of-speech tagger. In Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics, pages 17–20, 2014. gang Seeker, Mojg… [find](https://scholar.google.com/scholar?q=Dat+Quoc+Nguyen%2C+Dai+Quoc+Nguyen%2C+Dang+Duc+Pham%2C+and+Son+Bao+Pham%2E+RDRPOSTagger%3A+A+ripple+down+rules%2Dbased+part%2Dof%2Dspeech+tagger%2E+In+Proceedings+of+the+Demonstrations+at+the+14th+Conference+of+the+Eur)
24. Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter, Yoav Goldberg, Jan Hajič, Christopher Manning, Ryan McDonald, Slav Petrov, Sampo Pyysalo, Natalia Silveira, Reut Tsarfaty, and Daniel Zeman. Universal dependencies v1: A multilingual treebank collection. In Proceedings of the 10th Internation… [find](https://scholar.google.com/scholar?q=Joakim+Nivre%2C+Marie%2DCatherine+de+Marneffe%2C+Filip+Ginter%2C+Yoav+Goldberg%2C+Jan+Hajic%CC%8C%2C+Christopher+Manning%2C+Ryan+McDonald%2C+Slav+Petrov%2C+Sampo+Pyysalo%2C+Natalia+Silveira%2C+Reut+Tsarfaty%2C+and+Daniel+Zeman%2E+U) [our card](https://md.mova.elamur.ai/free/en/docs/papers/ud-2016-nivre-universal-dependencies-v1-multilingual-treebank/)
25. Geoffrey Nunberg. The Linguistics of Punctuation. Number 18 in CSLI Lecture Notes. Center for the Study of Language and Information, 1990. [find](https://scholar.google.com/scholar?q=Geoffrey+Nunberg%2E+The+Linguistics+of+Punctuation%2E+Number+18+in+CSLI+Lecture+Notes%2E+Center+for+the+Study+of+Language+and+Information%2C+1990%2E)
26. Mohammad Sadegh Rasooli and Michael Collins. Density-driven cross-lingual transfer of dependency parsers. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 328–338, 2015. [find](https://scholar.google.com/scholar?q=Mohammad+Sadegh+Rasooli+and+Michael+Collins%2E+Density%2Ddriven+cross%2Dlingual+transfer+of+dependency+parsers%2E+In+Proceedings+of+the+2015+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pag)
27. Joakim Nivre and Jens Nilsson. Pseudo-projective dependency parsing. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pages 99–106, 2005. [find](https://scholar.google.com/scholar?q=Joakim+Nivre+and+Jens+Nilsson%2E+Pseudo%2Dprojective+dependency+parsing%2E+In+Proceedings+of+the+43rd+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+pages+99%E2%80%93106%2C+2005%2E)
28. Mohammad Sadegh Rasooli and Joel R. Tetreault. Yara parser: A fast and accurate dependency parser. Computing Research Repository, arXiv:1503.06733 (version 2), 2015. — [arXiv:1503.06733](https://arxiv.org/abs/1503.06733) [find](https://scholar.google.com/scholar?q=Mohammad+Sadegh+Rasooli+and+Joel+R%2E+Tetreault%2E+Yara+parser%3A+A+fast+and+accurate+dependency+parser%2E+Computing+Research+Repository%2C+arXiv%3A1503%2E06733+%28version+2%29%2C+2015%2E+%E2%80%94+%5BarXiv%3A1503%2E06733%5D%28https%3A%2F%2Farxiv)
29. Joakim Nivre, Željko Agić, Maria Jesus Aranzabe, Masayuki Asahara, Aitziber Atutxa, Miguel Ballesteros, John Bauer, Kepa Bengoetxea, Riyaz Ahmad Bhat, Cristina Bosco, Sam Bowman, Giuseppe G. A. Celano, Miriam Connor, Marie-Catherine de Marneffe, Arantza Diaz de Ilarraza, Kaja Dobrovoljc, Timothy D… [find](https://scholar.google.com/scholar?q=Joakim+Nivre%2C+Z%CC%8Celjko+Agic%CC%81%2C+Maria+Jesus+Aranzabe%2C+Masayuki+Asahara%2C+Aitziber+Atutxa%2C+Miguel+Ballesteros%2C+John+Bauer%2C+Kepa+Bengoetxea%2C+Riyaz+Ahmad+Bhat%2C+Cristina+Bosco%2C+Sam+Bowman%2C+Giuseppe+G%2E+A%2E+Cela)
30. Alla Rozovskaya and Dan Roth. Training paradigms for correcting errors in grammar and usage. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 154–162, 2010. [find](https://scholar.google.com/scholar?q=Alla+Rozovskaya+and+Dan+Roth%2E+Training+paradigms+for+correcting+errors+in+grammar+and+usage%2E+In+Human+Language+Technologies%3A+The+2010+Annual+Conference+of+the+North+American+Chapter+of+the+Association)
31. Robert Sedgewick. Permutation generation methods. ACM Computing Surveys, 9(2):137–164, 1977. [find](https://scholar.google.com/scholar?q=Robert+Sedgewick%2E+Permutation+generation+methods%2E+ACM+Computing+Surveys%2C+9%282%29%3A137%E2%80%93164%2C+1977%2E)
32. Iulian Vlad Serban, Alberto Garcı́a-Durán, Caglar Gulcehre, Sungjin Ahn, Sarath Chandar, Aaron Courville, and Yoshua Bengio. Generating factoid questions with recurrent neural networks: The 30m factoid question-answer corpus. In Proceedings of the 54th Annual Meeting of the Association for Computat… [find](https://scholar.google.com/scholar?q=Iulian+Vlad+Serban%2C+Alberto+Garc%C4%B1%CC%81a%2DDura%CC%81n%2C+Caglar+Gulcehre%2C+Sungjin+Ahn%2C+Sarath+Chandar%2C+Aaron+Courville%2C+and+Yoshua+Bengio%2E+Generating+factoid+questions+with+recurrent+neural+networks%3A+The+30m+facto)
33. Patrice Y. Simard, Dave Steinkraus, and John C. Platt. Best practices for convolutional neural networks applied to visual document analysis. In Proceedings of the 7th International Conference on Document Analysis and Recognition, pages 958–, 2003. [find](https://scholar.google.com/scholar?q=Patrice+Y%2E+Simard%2C+Dave+Steinkraus%2C+and+John+C%2E+Platt%2E+Best+practices+for+convolutional+neural+networks+applied+to+visual+document+analysis%2E+In+Proceedings+of+the+7th+International+Conference+on+Docum)
34. Noah A. Smith and Jason Eisner. Annealing structural bias in multilingual weighted grammar induction. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pages 569–576, 2006. [find](https://scholar.google.com/scholar?q=Noah+A%2E+Smith+and+Jason+Eisner%2E+Annealing+structural+bias+in+multilingual+weighted+grammar+induction%2E+In+Proceedings+of+the+21st+International+Conference+on+Computational+Linguistics+and+44th+Annual+M)
35. Anders Søgaard. Data point selection for cross-language adaptation of dependency parsers. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 682–686, 2011. [find](https://scholar.google.com/scholar?q=Anders+S%C3%B8gaard%2E+Data+point+selection+for+cross%2Dlanguage+adaptation+of+dependency+parsers%2E+In+Proceedings+of+the+49th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%3A+Human+Language+Tec)
36. Valentin I. Spitkovsky. Grammar Induction and Parsing with Dependency-and-Boundary Models. PhD thesis, Computer Science Department, Stanford University, Stanford, CA, 2013. [find](https://scholar.google.com/scholar?q=Valentin+I%2E+Spitkovsky%2E+Grammar+Induction+and+Parsing+with+Dependency%2Dand%2DBoundary+Models%2E+PhD+thesis%2C+Computer+Science+Department%2C+Stanford+University%2C+Stanford%2C+CA%2C+2013%2E)
37. Valentin I. Spitkovsky, Hiyan Alshawi, and Daniel Jurafsky. Breaking out of local optima with count transforms and model recombination: A study in grammar induction. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1983–1995, 2013. [find](https://scholar.google.com/scholar?q=Valentin+I%2E+Spitkovsky%2C+Hiyan+Alshawi%2C+and+Daniel+Jurafsky%2E+Breaking+out+of+local+optima+with+count+transforms+and+model+recombination%3A+A+study+in+grammar+induction%2E+In+Proceedings+of+the+2013+Confere)
38. Matti Varjokallio and Dietrich Klakow. Unsupervised morph segmentation and statistical language models for vocabulary expansion. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 175–180, 2016. [find](https://scholar.google.com/scholar?q=Matti+Varjokallio+and+Dietrich+Klakow%2E+Unsupervised+morph+segmentation+and+statistical+language+models+for+vocabulary+expansion%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+the+Association+for+Comput)
39. Jesús Vilares, Manuel Vilares, and Juan Otero. Managing misspelled queries in IR applications. Information Processing & Management, 47(2):263–286, 2011. [find](https://scholar.google.com/scholar?q=Jesu%CC%81s+Vilares%2C+Manuel+Vilares%2C+and+Juan+Otero%2E+Managing+misspelled+queries+in+IR+applications%2E+Information+Processing+%26+Management%2C+47%282%29%3A263%E2%80%93286%2C+2011%2E)
40. Yushi Wang, Jonathan Berant, and Percy Liang. Building a semantic parser overnight. 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 Papers), pages 1332–1342, 2015. [find](https://scholar.google.com/scholar?q=Yushi+Wang%2C+Jonathan+Berant%2C+and+Percy+Liang%2E+Building+a+semantic+parser+overnight%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+and+the+7th+International)
41. Jason Weston, Antoine Bordes, Sumit Chopra, and Tomas Mikolov. Towards AI-complete question answering: A set of prerequisite toy tasks. In Proceedings of the International Conference on Learning Representations, 2016. [find](https://scholar.google.com/scholar?q=Jason+Weston%2C+Antoine+Bordes%2C+Sumit+Chopra%2C+and+Tomas+Mikolov%2E+Towards+AI%2Dcomplete+question+answering%3A+A+set+of+prerequisite+toy+tasks%2E+In+Proceedings+of+the+International+Conference+on+Learning+Repre) [our card](https://md.mova.elamur.ai/free/en/docs/papers/weston-2015-towards-ai-complete-question-answering/)