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In countries that speak multiple main languages, mixing up different languages within a conversation is commonly called code-switching.
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John D. Lafferty, Andrew McCallum, and Fernando Pereira. 2001 · 2001
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Learning character-level representations for part-of-speech tagging
Cícero Nogueira dos Santos and Bianca Zadrozny. 2014 · 2014
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Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Charagram: Embedding words and sentences via character n-grams
John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2016 · 2016
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Learning word meta-embeddings
Wenpeng Yin and Hinrich Schütze. 2016 · 2016
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Word translation without parallel data
Alexis Conneau, Guillaume Lample, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou. 2017 · 2017
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Linear ensembles of word embedding models
Avo Muromägi, Kairit Sirts, and Sven Laur. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Named entity recognition on code-switched data: Overview of the calcs 2018 shared task
Gustavo Aguilar, Fahad AlGhamdi, Victor Soto, Mona Diab, Julia Hirschberg, and Thamar Solorio. 2018 · 2018
Cited alongside, same era.
Think globally, embed locally: locally linear meta-embedding of words
Danushka Bollegala, Kohei Hayashi, and Ken-Ichi Kawarabayashi. 2018 · 2018
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Bpemb: Tokenization-free pre-trained subword embeddings in 275 languages
Benjamin Heinzerling and Michael Strube. 2018 · 2018
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Dynamic meta-embeddings for improved sentence representations
Douwe Kiela, Changhan Wang, and Kyunghyun Cho. 2018 · 2018
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Iit (bhu) submission for the acl shared task on named entity recognition on code-switched data
Shashwat Trivedi, Harsh Rangwani, and Anil Kumar Singh. 2018 · 2018
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Code-switched named entity recognition with embedding attention
Changhan Wang, Kyunghyun Cho, and Douwe Kiela. 2018 · 2018
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Emo2vec: Learning generalized emotion representation by multi-task training
Peng Xu, Andrea Madotto, Chien-Sheng Wu, Ji Ho Park, and Pascale Fung. 2018 · 2018
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Incorporating word and subword units in unsupervised machine translation using language model rescoring
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Frustratingly easy meta-embedding–computing meta-embeddings by averaging source word embeddings
Joshua Coates and Danushka Bollegala. 2018 · 2018
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Learning word vectors for 157 languages
Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, and Tomas Mikolov. 2018 · 2018
Cited alongside, same era.
Code-switching language modeling using syntax-aware multi-task learning
Genta Indra Winata, Andrea Madotto, Chien-Sheng Wu, and Pascale Fung. 2018a
Cited in the paper.
Bilingual character representation for efficiently addressing out-of-vocabulary words in code-switching named entity recognition
Genta Indra Winata, Chien-Sheng Wu, Andrea Madotto, and Pascale Fung. 2018b
Cited in the paper.
Zihan Liu, Yan Xu, Genta Indra Winata, and Pascale Fung. 2019 · 2019
Closest in time.
Learning multilingual meta-embeddings for code-switching named entity recognition
Genta Indra Winata, Zhaojiang Lin, and Pascale Fung. 2019 · 2019
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