---
title: "Massively Multilingual Word Embeddings — Ammar 2016"
description: "Earlier work on cross-lingual word vectors mostly covered language pairs and often depended on parallel text."
authors: ["Waleed Ammar", "George Mulcaire", "Yulia Tsvetkov", "Guillaume Lample", "Chris Dyer", "Noah A. Smith"]
year: 2016
venue: "arXiv:1602.01925"
source: "https://arxiv.org/abs/1602.01925"
doi: "10.48550/arxiv.1602.01925"
arxiv: "1602.01925"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ammar-2016-massively-multilingual-word-embeddings/"
---
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# Massively Multilingual Word Embeddings

**Authors:** [Waleed Ammar](https://md.research.elamur.ai/free/en/authors/waleed-ammar/), [George Mulcaire](https://md.research.elamur.ai/free/en/authors/george-mulcaire/), [Yulia Tsvetkov](https://md.research.elamur.ai/free/en/authors/yulia-tsvetkov/), [Guillaume Lample](https://md.research.elamur.ai/free/en/authors/guillaume-lample/), [Chris Dyer](https://md.research.elamur.ai/free/en/authors/chris-dyer/), [Noah A. Smith](https://md.research.elamur.ai/free/en/authors/noah-a-smith/) · **Year:** 2016 · **Venue:** arXiv:1602.01925 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1602.01925) · [PDF](https://arxiv.org/pdf/1602.01925) · [DOI 10.48550/arxiv.1602.01925](https://doi.org/10.48550/arxiv.1602.01925) · [arXiv 1602.01925](https://arxiv.org/abs/1602.01925) · [OpenAlex](https://openalex.org/W2270364989)

**Citations:** 263 citations (openalex-snapshot · 2026-10-14) · [135](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Earlier work on cross-lingual word vectors mostly covered language pairs and often depended on parallel text. This paper places words from more than fifty languages into a single common vector space using only monolingual corpora and bilingual dictionaries. It introduces two estimators:

- **multiCluster** links translation-equivalent words into dictionary-derived clusters. It then trains skipgram on corpora in which each token is replaced by its cluster ID.
- **multiCCA** learns CCA projections from bilingual dictionaries and maps each language's monolingual embeddings into the English space.

Both are trained for 59 languages. They are compared with two parallel-data baselines, multiSkip and translation-invariance. For evaluation, the authors adapt QVEC to the multilingual case (multiQVEC) and propose multiQVEC-CCA, which removes a theoretical flaw in the original metric. Both new metrics track downstream results (dependency parsing and document classification) more closely than earlier intrinsic measures. A public evaluation portal and open-source code give the field a shared benchmark. This is especially valuable for transferring models to lower-resource languages.

## Key points
- multiCluster and multiCCA need no parallel corpora. When parallel text is available, dictionaries can be extracted from it with fast_align, using a translation-probability threshold of τ = 0.1.
- multiCCA uses English as the pivot space because English has the largest corpora and the most bilingual dictionaries.
- With 59 languages, multiCCA beats multiCluster on all metrics. Merging translations into one vector adds semantic ambiguity, which hurts multiCluster at that scale.
- The 12-language comparison covers English, French, German, Spanish, Swedish, Italian, Danish, Finnish, Hungarian, Czech, Greek and Bulgarian. In it, a dictionary-based method wins five evaluations, including both extrinsic tasks. Parsing scores were 61.0 for multiCluster versus 59.8 for translation-invariance, and multiCluster and multiCCA tied at 92.1 on document classification. multiSkip never ranks first.
- The web portal scores uploaded embeddings on nine metrics and has separate development and test modes.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

## Cited by

Cited by

135 papers of our library cite it (of the 140673 whose bibliographies we read); 11 of them have a page here, 124 not yet.

- [Many Languages, One Parser](https://md.research.elamur.ai/free/en/papers/ammar-2016-many-languages-one-parser/)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://md.research.elamur.ai/free/en/papers/artetxe-2018-massively-multilingual-sentence-embeddings-zero/)
- [Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus](https://md.research.elamur.ai/free/en/papers/caswell-2020-language-id-wild-unexpected/)
- [Challenges and Strategies in Cross-Cultural NLP](https://md.research.elamur.ai/free/en/papers/hershcovich-2022-challenges-strategies-cross-cultural-nlp/)
- [Neural Cross-Lingual Coreference Resolution And Its Application To Entity Linking](https://md.research.elamur.ai/free/en/papers/kundu-2018-neural-cross-lingual-coreference-resolution/)
- [MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark](https://md.research.elamur.ai/free/en/papers/li-2021-mtop-comprehensive-multilingual-task-oriented/)
- [Morphology-rich Alphasyllabary Embeddings](https://md.research.elamur.ai/free/en/papers/lingbulk-2020-mersha-morphology-rich-alphasyllabary-embeddings/)
- [When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models](https://md.research.elamur.ai/free/en/papers/muller-2021-when-being-unseen-mbert-is/)
- [A Corpus for Multilingual Document Classification in Eight Languages](https://md.research.elamur.ai/free/en/papers/schwenk-2018-corpus-multilingual-document-classification-eight/)
- [Offline bilingual word vectors, orthogonal transformations and the inverted softmax](https://md.research.elamur.ai/free/en/papers/smith-2017-offline-bilingual-word-vectors-orthogonal/)
- [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://md.research.elamur.ai/free/en/papers/wu-2019-beto-bentz-becas-surprising-cross/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{ammar2016massively,
  title = {{Massively Multilingual Word Embeddings}},
  author = {Ammar, Waleed and Mulcaire, George and Tsvetkov, Yulia and Lample, Guillaume and Dyer, Chris and Smith, Noah A.},
  year = {2016},
  doi = {10.48550/arxiv.1602.01925},
  eprint = {1602.01925},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1602.01925}
}
```

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## References

38 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Željko Agić, Maria Jesus Aranzabe, Aitziber Atutxa, Cristina Bosco, Jinho Choi, Marie-Catherine de Marneffe, Timothy Dozat, Richárd Farkas, Jennifer Foster, Filip Ginter, Iakes Goenaga, Koldo Gojenola, Yoav Goldberg, Jan Hajič, Anders Trærup Johannsen, Jenna Kanerva, Juha Kuokkala, Veronika Laip… [find](https://scholar.google.com/scholar?q=Z%CC%8Celjko+Agic%CC%81%2C+Maria+Jesus+Aranzabe%2C+Aitziber+Atutxa%2C+Cristina+Bosco%2C+Jinho+Choi%2C+Marie%2DCatherine+de+Marneffe%2C+Timothy+Dozat%2C+Richa%CC%81rd+Farkas%2C+Jennifer+Foster%2C+Filip+Ginter%2C+Iakes+Goenaga%2C+Koldo+Gojen)
2. Eneko Agirre, Enrique Alfonseca, Keith Hall, Jana Kravalova, Marius Paşca, and Aitor Soroa. 2009. A study on similarity and relatedness using distributional and WordNet-based approaches. In Proc. of NAACL, pages 19–27. [find](https://scholar.google.com/scholar?q=Eneko+Agirre%2C+Enrique+Alfonseca%2C+Keith+Hall%2C+Jana+Kravalova%2C+Marius+Pas%CC%A7ca%2C+and+Aitor+Soroa%2E+2009%2E+A+study+on+similarity+and+relatedness+using+distributional+and+WordNet%2Dbased+approaches%2E+In+Proc%2E+of+)
3. Rami Al-Rfou’, Bryan Perozzi, and Steven Skiena. 2013. Polyglot: Distributed word representations for multilingual nlp. In CONLL. [find](https://scholar.google.com/scholar?q=Rami+Al%2DRfou%E2%80%99%2C+Bryan+Perozzi%2C+and+Steven+Skiena%2E+2013%2E+Polyglot%3A+Distributed+word+representations+for+multilingual+nlp%2E+In+CONLL%2E) [our card](https://md.research.elamur.ai/free/en/papers/rfou-2013-polyglot-distributed-word-representations-multilingual/)
4. Elia Bruni, Nam-Khanh Tran, and Marco Baroni. 2014. Multimodal distributional semantics. JAIR. [find](https://scholar.google.com/scholar?q=Elia+Bruni%2C+Nam%2DKhanh+Tran%2C+and+Marco+Baroni%2E+2014%2E+Multimodal+distributional+semantics%2E+JAIR%2E)
5. José Camacho-Collados, Mohammad Taher Pilehvar, and Roberto Navigli. 2015. A framework for the construction of monolingual and cross-lingual word similarity datasets. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Jose%CC%81+Camacho%2DCollados%2C+Mohammad+Taher+Pilehvar%2C+and+Roberto+Navigli%2E+2015%2E+A+framework+for+the+construction+of+monolingual+and+cross%2Dlingual+word+similarity+datasets%2E+In+Proc%2E+of+ACL%2E)
6. Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using rnn encoder-decoder for statistical machine translation. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=Kyunghyun+Cho%2C+Bart+Van+Merrie%CC%88nboer%2C+Caglar+Gulcehre%2C+Dzmitry+Bahdanau%2C+Fethi+Bougares%2C+Holger+Schwenk%2C+and+Yoshua+Bengio%2E+2014%2E+Learning+phrase+representations+using+rnn+encoder%2Ddecoder+for+statisti)
7. Jocelyn Coulmance, Jean-Marc Marty, Guillaume Wenzek, and Amine Benhalloum. 2015. Trans-gram, fast cross-lingual word-embeddings. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=Jocelyn+Coulmance%2C+Jean%2DMarc+Marty%2C+Guillaume+Wenzek%2C+and+Amine+Benhalloum%2E+2015%2E+Trans%2Dgram%2C+fast+cross%2Dlingual+word%2Dembeddings%2E+In+Proc%2E+of+EMNLP%2E)
8. Chris Dyer, Victor Chahuneau, and Noah A. Smith. 2013. A simple, fast, and effective reparameterization of IBM Model 2. In Proc. of NAACL. [find](https://scholar.google.com/scholar?q=Chris+Dyer%2C+Victor+Chahuneau%2C+and+Noah+A%2E+Smith%2E+2013%2E+A+simple%2C+fast%2C+and+effective+reparameterization+of+IBM+Model+2%2E+In+Proc%2E+of+NAACL%2E)
9. Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, and Noah A Smith. 2015. Transition-based dependency parsing with stack long short-term memory. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Chris+Dyer%2C+Miguel+Ballesteros%2C+Wang+Ling%2C+Austin+Matthews%2C+and+Noah+A+Smith%2E+2015%2E+Transition%2Dbased+dependency+parsing+with+stack+long+short%2Dterm+memory%2E+In+Proc%2E+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/missing-2015-dyer-transition-based-dependency-parsing-stack/)
10. Manaal Faruqui and Chris Dyer. 2014. Improving vector space word representations using multilingual correlation. Proc. of EACL. [find](https://scholar.google.com/scholar?q=Manaal+Faruqui+and+Chris+Dyer%2E+2014%2E+Improving+vector+space+word+representations+using+multilingual+correlation%2E+Proc%2E+of+EACL%2E)
11. Matt Gardner, Kejun Huang, Evangelos Papalexakis, Xiao Fu, Partha Talukdar, Christos Faloutsos, Nicholas Sidiropoulos, and Tom Mitchell. 2015. Translation invariant word embeddings. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=Matt+Gardner%2C+Kejun+Huang%2C+Evangelos+Papalexakis%2C+Xiao+Fu%2C+Partha+Talukdar%2C+Christos+Faloutsos%2C+Nicholas+Sidiropoulos%2C+and+Tom+Mitchell%2E+2015%2E+Translation+invariant+word+embeddings%2E+In+Proc%2E+of+EMNLP%2E)
12. Stephan Gouws, Yoshua Bengio, and Greg Corrado. 2014. Bilbowa: Fast bilingual distributed representations without word alignments. arXiv preprint arXiv:1410.2455. — [arXiv:1410.2455](https://arxiv.org/abs/1410.2455) [find](https://scholar.google.com/scholar?q=Stephan+Gouws%2C+Yoshua+Bengio%2C+and+Greg+Corrado%2E+2014%2E+Bilbowa%3A+Fast+bilingual+distributed+representations+without+word+alignments%2E+arXiv+preprint+arXiv%3A1410%2E2455%2E+%E2%80%94+%5BarXiv%3A1410%2E2455%5D%28https%3A%2F%2Farxiv%2Eorg)
13. Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, and Ting Liu. 2015. Cross-lingual dependency parsing based on distributed representations. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Jiang+Guo%2C+Wanxiang+Che%2C+David+Yarowsky%2C+Haifeng+Wang%2C+and+Ting+Liu%2E+2015%2E+Cross%2Dlingual+dependency+parsing+based+on+distributed+representations%2E+In+Proc%2E+of+ACL%2E)
14. Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, and Ting Liu. 2016. A representation learning framework for multi-source transfer parsing. In Proc. of AAAI. [find](https://scholar.google.com/scholar?q=Jiang+Guo%2C+Wanxiang+Che%2C+David+Yarowsky%2C+Haifeng+Wang%2C+and+Ting+Liu%2E+2016%2E+A+representation+learning+framework+for+multi%2Dsource+transfer+parsing%2E+In+Proc%2E+of+AAAI%2E)
15. Michael U Gutmann and Aapo Hyvärinen. 2012. Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. JMLR. [find](https://scholar.google.com/scholar?q=Michael+U+Gutmann+and+Aapo+Hyva%CC%88rinen%2E+2012%2E+Noise%2Dcontrastive+estimation+of+unnormalized+statistical+models%2C+with+applications+to+natural+image+statistics%2E+JMLR%2E)
16. Aria Haghighi, Percy Liang, Taylor Berg-Kirkpatrick, and Dan Klein. 2008. Learning bilingual lexicons from monolingual corpora. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Aria+Haghighi%2C+Percy+Liang%2C+Taylor+Berg%2DKirkpatrick%2C+and+Dan+Klein%2E+2008%2E+Learning+bilingual+lexicons+from+monolingual+corpora%2E+In+Proc%2E+of+ACL%2E)
17. Karl Moritz Hermann and Phil Blunsom. 2014. Multilingual Models for Compositional Distributional Semantics. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Karl+Moritz+Hermann+and+Phil+Blunsom%2E+2014%2E+Multilingual+Models+for+Compositional+Distributional+Semantics%2E+In+Proc%2E+of+ACL%2E)
18. Alexandre Klementiev, Ivan Titov, and Binod Bhattarai. 2012. Inducing crosslingual distributed representations of words. In Proc. of COLING. [find](https://scholar.google.com/scholar?q=Alexandre+Klementiev%2C+Ivan+Titov%2C+and+Binod+Bhattarai%2E+2012%2E+Inducing+crosslingual+distributed+representations+of+words%2E+In+Proc%2E+of+COLING%2E)
19. Tomáš Kociskỳ, Karl Moritz Hermann, and Phil Blunsom. 2014. Learning bilingual word representations by marginalizing alignments. In arXiv preprint arXiv:1405.0947. [find](https://scholar.google.com/scholar?q=Toma%CC%81s%CC%8C+Kocisky%CC%80%2C+Karl+Moritz+Hermann%2C+and+Phil+Blunsom%2E+2014%2E+Learning+bilingual+word+representations+by+marginalizing+alignments%2E+In+arXiv+preprint+arXiv%3A1405%2E0947%2E)
20. Ira Leviant and Roi Reichart. 2015. Judgment language matters: Towards judgment language informed vector space modeling. In arXiv preprint arXiv:1508.00106. — [arXiv:1508.00106](https://arxiv.org/abs/1508.00106) [find](https://scholar.google.com/scholar?q=Ira+Leviant+and+Roi+Reichart%2E+2015%2E+Judgment+language+matters%3A+Towards+judgment+language+informed+vector+space+modeling%2E+In+arXiv+preprint+arXiv%3A1508%2E00106%2E+%E2%80%94+%5BarXiv%3A1508%2E00106%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F)
21. Minh-Thang Luong, Richard Socher, and Christopher D. Manning. 2013. Better word representations with recursive neural networks for morphology. In Proc. of CoNLL. [find](https://scholar.google.com/scholar?q=Minh%2DThang+Luong%2C+Richard+Socher%2C+and+Christopher+D%2E+Manning%2E+2013%2E+Better+word+representations+with+recursive+neural+networks+for+morphology%2E+In+Proc%2E+of+CoNLL%2E) [our card](https://md.research.elamur.ai/free/en/papers/luong-2013-better-word-representations-recursive-neural/)
22. Minh-Thang Luong, Ilya Sutskever, Quoc V Le, Oriol Vinyals, and Wojciech Zaremba. 2015a. Addressing the rare word problem in neural machine translation. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Minh%2DThang+Luong%2C+Ilya+Sutskever%2C+Quoc+V+Le%2C+Oriol+Vinyals%2C+and+Wojciech+Zaremba%2E+2015a%2E+Addressing+the+rare+word+problem+in+neural+machine+translation%2E+In+Proc%2E+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/luong-2015-addressing-rare-word-problem-neural/)
23. Thang Luong, Hieu Pham, and Christopher D Manning. 2015b. Bilingual word representations with monolingual quality in mind. In Proc. of NAACL. [find](https://scholar.google.com/scholar?q=Thang+Luong%2C+Hieu+Pham%2C+and+Christopher+D+Manning%2E+2015b%2E+Bilingual+word+representations+with+monolingual+quality+in+mind%2E+In+Proc%2E+of+NAACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/luong-2015-bilingual-word-representations-monolingual-quality/)
24. Héctor Martı́nez Alonso, Anders Johannsen, Sussi Olsen, Sanni Nimb, Nicolai Hartvig Sørensen, Anna Braasch, Anders Søgaard, and Bolette Sandford Pedersen. 2015. Supersense tagging for Danish. In Proc. of NODAL-IDA, page 21. [find](https://scholar.google.com/scholar?q=He%CC%81ctor+Mart%C4%B1%CC%81nez+Alonso%2C+Anders+Johannsen%2C+Sussi+Olsen%2C+Sanni+Nimb%2C+Nicolai+Hartvig+S%C3%B8rensen%2C+Anna+Braasch%2C+Anders+S%C3%B8gaard%2C+and+Bolette+Sandford+Pedersen%2E+2015%2E+Supersense+tagging+for+Danish%2E+In+Proc)
25. Héctor Martı́nez Alonso, Anders Johannsen, Sussi Olsen, Sanni Nimb, and Bolette Sandford Pedersen. 2016. An empirically grounded expansion of the supersense inventory. In Proc. of the Global Wordnet Conference. [find](https://scholar.google.com/scholar?q=He%CC%81ctor+Mart%C4%B1%CC%81nez+Alonso%2C+Anders+Johannsen%2C+Sussi+Olsen%2C+Sanni+Nimb%2C+and+Bolette+Sandford+Pedersen%2E+2016%2E+An+empirically+grounded+expansion+of+the+supersense+inventory%2E+In+Proc%2E+of+the+Global+Wordnet+)
26. Ryan McDonald, Slav Petrov, and Keith Hall. 2011. Multi-source transfer of delexicalized dependency parsers. In Proc. of EMNLP, pages 62–72. [find](https://scholar.google.com/scholar?q=Ryan+McDonald%2C+Slav+Petrov%2C+and+Keith+Hall%2E+2011%2E+Multi%2Dsource+transfer+of+delexicalized+dependency+parsers%2E+In+Proc%2E+of+EMNLP%2C+pages+62%E2%80%9372%2E) [our card](https://md.research.elamur.ai/free/en/papers/mcdonald-2011-multi-source-transfer-delexicalized-dependency/)
27. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a. Efficient estimation of word representations in vector space. In Proc. of ICLR. [find](https://scholar.google.com/scholar?q=Tomas+Mikolov%2C+Kai+Chen%2C+Greg+Corrado%2C+and+Jeffrey+Dean%2E+2013a%2E+Efficient+estimation+of+word+representations+in+vector+space%2E+In+Proc%2E+of+ICLR%2E)
28. Tomas Mikolov, Quoc V. Le, and Ilya Sutskever. 2013b. Exploiting similarities among languages for machine translation. In arXiv preprint arXiv:1309.4168v1. [find](https://scholar.google.com/scholar?q=Tomas+Mikolov%2C+Quoc+V%2E+Le%2C+and+Ilya+Sutskever%2E+2013b%2E+Exploiting+similarities+among+languages+for+machine+translation%2E+In+arXiv+preprint+arXiv%3A1309%2E4168v1%2E)
29. George A. Miller, Claudia Leacock, Randee Tengi, and Ross T. Bunker. 1993. A semantic concordance. In Proc. of HLT, pages 303–308. [find](https://scholar.google.com/scholar?q=George+A%2E+Miller%2C+Claudia+Leacock%2C+Randee+Tengi%2C+and+Ross+T%2E+Bunker%2E+1993%2E+A+semantic+concordance%2E+In+Proc%2E+of+HLT%2C+pages+303%E2%80%93308%2E)
30. Simonetta Montemagni, Francesco Barsotti, Marco Battista, Nicoletta Calzolari, Ornella Corazzari, Alessandro Lenci, Antonio Zampolli, Francesca Fanciulli, Maria Mas-setani, Remo Raffaelli, et al. 2003. Building the italian syntactic-semantic treebank. In Treebanks, pages 189–210. Springer. [find](https://scholar.google.com/scholar?q=Simonetta+Montemagni%2C+Francesco+Barsotti%2C+Marco+Battista%2C+Nicoletta+Calzolari%2C+Ornella+Corazzari%2C+Alessandro+Lenci%2C+Antonio+Zampolli%2C+Francesca+Fanciulli%2C+Maria+Mas%2Dsetani%2C+Remo+Raffaelli%2C+et+al%2E+2003)
31. Franz Joseph Och. 1999. An efficient method for determining bilingual word classes. In EACL. [find](https://scholar.google.com/scholar?q=Franz+Joseph+Och%2E+1999%2E+An+efficient+method+for+determining+bilingual+word+classes%2E+In+EACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/och-1999-efficient-method-determining-bilingual-word/)
32. Slav Petrov, Dipanjan Das, and Ryan McDonald. 2012. A universal part-of-speech tagset. In Proc. of LREC. [find](https://scholar.google.com/scholar?q=Slav+Petrov%2C+Dipanjan+Das%2C+and+Ryan+McDonald%2E+2012%2E+A+universal+part%2Dof%2Dspeech+tagset%2E+In+Proc%2E+of+LREC%2E)
33. Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014. Intriguing properties of neural networks. In Proc. of ICLR. [find](https://scholar.google.com/scholar?q=Christian+Szegedy%2C+Wojciech+Zaremba%2C+Ilya+Sutskever%2C+Joan+Bruna%2C+Dumitru+Erhan%2C+Ian+Goodfellow%2C+and+Rob+Fergus%2E+2014%2E+Intriguing+properties+of+neural+networks%2E+In+Proc%2E+of+ICLR%2E) [our card](https://md.research.elamur.ai/free/en/papers/szegedy-2013-intriguing-properties-neural-networks/)
34. Oscar Täckström, Ryan Mc-Donald, and Jakob Uszkoreit. 2012. Cross-lingual word clusters for direct transfer of linguistic structure. In Proc. of NAACL, pages 477–487. [find](https://scholar.google.com/scholar?q=Oscar+Ta%CC%88ckstro%CC%88m%2C+Ryan+Mc%2DDonald%2C+and+Jakob+Uszkoreit%2E+2012%2E+Cross%2Dlingual+word+clusters+for+direct+transfer+of+linguistic+structure%2E+In+Proc%2E+of+NAACL%2C+pages+477%E2%80%93487%2E) [our card](https://md.research.elamur.ai/free/en/papers/tackstrom-2012-cross-lingual-word-clusters-direct/)
35. Yulia Tsvetkov, Leonid Boytsov, Anatole Gershman, Eric Nyberg, and Chris Dyer. 2014. Metaphor detection with cross-lingual model transfer. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Yulia+Tsvetkov%2C+Leonid+Boytsov%2C+Anatole+Gershman%2C+Eric+Nyberg%2C+and+Chris+Dyer%2E+2014%2E+Metaphor+detection+with+cross%2Dlingual+model+transfer%2E+In+Proc%2E+of+ACL%2E)
36. Yulia Tsvetkov, Manaal Faruqui, Wang Ling, Guillaume Lample, and Chris Dyer. 2015. Evaluation of word vector representations by subspace alignment. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=Yulia+Tsvetkov%2C+Manaal+Faruqui%2C+Wang+Ling%2C+Guillaume+Lample%2C+and+Chris+Dyer%2E+2015%2E+Evaluation+of+word+vector+representations+by+subspace+alignment%2E+In+Proc%2E+of+EMNLP%2E)
37. Daniel Zeman and Philip Resnik. 2008. Cross-language parser adaptation between related languages. In Proc. of IJCNLP, pages 35–42. [find](https://scholar.google.com/scholar?q=Daniel+Zeman+and+Philip+Resnik%2E+2008%2E+Cross%2Dlanguage+parser+adaptation+between+related+languages%2E+In+Proc%2E+of+IJCNLP%2C+pages+35%E2%80%9342%2E)
38. Will Y Zou, Richard Socher, Daniel M Cer, and Christopher D Manning. 2013. Bilingual word embeddings for phrase-based machine translation. In Proc. of EMNLP, pages 1393–1398. [find](https://scholar.google.com/scholar?q=Will+Y+Zou%2C+Richard+Socher%2C+Daniel+M+Cer%2C+and+Christopher+D+Manning%2E+2013%2E+Bilingual+word+embeddings+for+phrase%2Dbased+machine+translation%2E+In+Proc%2E+of+EMNLP%2C+pages+1393%E2%80%931398%2E)