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Cross-lingual model transfer is a compelling and popular method for predicting annotations in a low-resource language, whereby parallel corpora provide a bridge to a high-resource language and its associated annotated corpora.
Cross-lingual morphological tagging for low-resource languages
Jan Buys and Jan A. Botha. 2016 · 1964
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John Lafferty, Andrew McCallum, and Fernando Pereira. 2001 · 2001
Earlier work this paper cites.
Inducing multilingual POS taggers and NP brackets via robust projection across aligned corpora
David Yarowsky and Grace Ngai. 2001 · 2001
Earlier work this paper cites.
Conll-x shared task on multilingual dependency parsing
Sabine Buchholz and Erwin Marsi. 2006 · 2006
Earlier work this paper cites.
Recurrent neural network based language model
Tomas Mikolov, Martin Karafiát, Lukas Burget, Jan Cernockỳ, and Sanjeev Khudanpur. 2010 · 2010
Earlier work this paper cites.
Unsupervised part-of-speech tagging with bilingual graph-based projections
Dipanjan Das and Slav Petrov. 2011 · 2011
Earlier work this paper cites.
Learning a part-of-speech tagger from two hours of annotation
Dan Garrette and Jason Baldridge. 2013 · 2013
Cited alongside, same era.
Token and type constraints for cross-lingual part-of-speech tagging
Oscar Täckström, Dipanjan Das, Slav Petrov, Ryan McDonald, and Joakim Nivre. 2013 · 2013
Cited alongside, same era.
Panlex: Building a resource for panlingual lexical translation
David Kamholz, Jonathan Pool, and Susan M Colowick. 2014 · 2014
Cited alongside, same era.
Cross-lingual dependency parsing based on distributed representations
Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, and Ting Liu. 2015 · 2015
Cited alongside, same era.
Bidirectional lstm-crf models for sequence tagging
Zhiheng Huang, Wei Xu, and Kai Yu. 2015 · 2015
Cited alongside, same era.
Simple semi-supervised pos tagging
Multilingual projection for parsing truly low-resource languages
Željko Agić, Anders Johannsen, Barbara Plank, Héctor Alonso Martínez, Natalie Schluter, and Anders Søgaard. 2016 · 2016
Later among the works it cites.
Massively multilingual word embeddings
Waleed Ammar, George Mulcaire, Yulia Tsvetkov, Guillaume Lample, Chris Dyer, and Noah A Smith. 2016 · 2016
Later among the works it cites.
Learning when to trust distant supervision: An application to low-resource pos tagging using cross-lingual projection
Meng Fang and Trevor Cohn. 2016 · 2016
Later among the works it cites.
Ten pairs to tag–multilingual pos tagging via coarse mapping between embeddings
Yuan Zhang, David Gaddy, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
Later among the works it cites.
Dynet: The dynamic neural network toolkit
Graham Neubig, Chris Dyer, Yoav Goldberg, Austin Matthews, Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, David Chiang, Daniel Clothiaux, Trevor Cohn, Kevin Duh, Manaal Faruqui, Cynthia Gan, Dan Garrette, Yangfeng Ji, Lingpeng Kong, Adhiguna Kuncoro, Gaurav Kumar, Chaitanya Malaviya, Paul Michel, Yusuke Oda, Matthew Richardson, Naomi Saphra, Swabha Swayamdipta, and Pengcheng Yin. 2017 · 2017
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Karl Stratos and Michael Collins. 2015 · 2015
Cited alongside, same era.
Closest in time.
A universal part-of-speech tagset
Slav Petrov, Dipanjan Das, and Ryan McDonald. 2011 · 2086
Closest in time.