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While neural networks have been used extensively to make substantial progress in the machine translation task, they are known for being heavily dependent on the availability of large amounts of training data.
Improving lstm-based video description with linguistic knowledge mined from text
Subhashini Venugopalan, Lisa Anne Hendricks, Raymond Mooney, and Kate Saenko. 2016 · 1966
Earlier work this paper cites.
Building a large-scale knowledge base for machine translation
Kevin Knight and Steve K Luk. 1994 · 1994
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
BLEU: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
METEOR: An automatic metric for MT evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
Earlier work this paper cites.
Europarl: A parallel corpus for statistical machine translation
Philipp Koehn. 2005 · 2005
Earlier work this paper cites.
The jrc-acquis: A multilingual aligned parallel corpus with 20+ languages
Ralf Steinberger, Bruno Pouliquen, Anna Widiger, Camelia Ignat, Tomaz Erjavec, Dan Tufis, and Dániel Varga. 2006 · 2006
Earlier work this paper cites.
Dbpedia: A nucleus for a web of open data
Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 2007 · 2007
Earlier work this paper cites.
Moses: Open source toolkit for statistical machine translation
Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, et al. 2007 · 2007
Earlier work this paper cites.
BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network
Roberto Navigli and Simone Paolo Ponzetto. 2012 · 2012
Earlier work this paper cites.
Parallel data, tools and interfaces in opus
Jörg Tiedemann. 2012 · 2012
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
Earlier work this paper cites.
Mining translations from the web of open linked data
John Philip McCrae and Philipp Cimiano. 2013 · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Earlier work this paper cites.
Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch. 2014 · 2014
Earlier work this paper cites.
Knowledge graph and text jointly embedding
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014 · 2014
Earlier work this paper cites.
Knowledge Portability with Semantic Expansion of Ontology Labels
Mihael Arcan, Marco Turchi, and Paul Buitelaar. 2015 · 2015
Earlier work this paper cites.
Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
GERBIL: general entity annotator benchmarking framework
Ricardo Usbeck, Michael Röder, Axel-Cyrille Ngonga Ngomo, Ciro Baron, Andreas Both, Martin Brümmer, Diego Ceccarelli, Marco Cornolti, Didier Cherix, Bernd Eickmann, Paolo Ferragina, Christiane Lemke, Andrea Moro, Roberto Navigli, Francesco Piccinno, Giuseppe Rizzo, Harald Sack, René Speck, Raphaël Troncy, Jörg Waitelonis, and Lars Wesemann. 2015 · 2015
Cited alongside, same era.
Transg: A generative mixture model for knowledge graph embedding
Han Xiao, Minlie Huang, Yu Hao, and Xiaoyan Zhu. 2015 · 2015
Cited alongside, same era.
Aligning knowledge and text embeddings by entity descriptions
Huaping Zhong, Jianwen Zhang, Zhen Wang, Hai Wan, and Zheng Chen. 2015 · 2015
Cited alongside, same era.
Using babelnet to improve OOV coverage in SMT
Jinhua Du, Andy Way, and Andrzej Zydron. 2016 · 2016
Conceptnet at semeval-2017 task 2: Extending word embeddings with multilingual relational knowledge
Robert Speer and Joanna Lowry-Duda. 2017 · 2017
Later among the works it cites.
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
Later among the works it cites.
Ssp: Semantic space projection for knowledge graph embedding with text descriptions
Han Xiao, Minlie Huang, Lian Meng, and Xiaoyan Zhu. 2017 · 2017
Later among the works it cites.
Leveraging knowledge bases in lstms for improving machine reading
Bishan Yang and Tom Mitchell. 2017 · 2017
Later among the works it cites.
Comparative study of cnn and rnn for natural language processing
Wenpeng Yin, Katharina Kann, Mo Yu, and Hinrich Schütze. 2017 · 2017
Later among the works it cites.
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Cited alongside, same era.
Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor OK Li. 2016 · 2016
Cited alongside, same era.
Neural name translation improves neural machine translation
Xiaoqing Li, Jiajun Zhang, and Chengqing Zong. 2016 · 2016
Cited alongside, same era.
Assessing the ability of lstms to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016 · 2016
Cited alongside, same era.
Achieving open vocabulary neural machine translation with hybrid word-character models
Minh-Thang Luong and Christopher D. Manning. 2016 · 2016
Cited alongside, same era.
Linguistic input features improve neural machine translation
Rico Sennrich and Barry Haddow. 2016 · 2016
Cited alongside, same era.
Knowledge-Based Semantic Embedding for Machine Translation
Chen Shi, Shujie Liu, Shuo Ren, Shi Feng, Mu Li, Ming Zhou, Xu Sun, and Houfeng Wang. 2016 · 2016
Cited alongside, same era.
Towards Semantic-based Hybrid Machine Translation between Bulgarian and English
Kiril Simov, Petya Osenova, and Alex Popov. 2016 · 2016
Cited alongside, same era.
Learning beyond datasets: Knowledge graph augmented neural networks for natural language processing
Annervaz, Somnath Basu Roy Chowdhury, and Ambedkar Dukkipati. 2018 · 2018
Later among the works it cites.
Understanding back-translation at scale
Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018 · 2018
Later among the works it cites.
Learning word vectors for 157 languages
Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, and Tomas Mikolov. 2018 · 2018
Later among the works it cites.
Iterative back-translation for neural machine translation
Vu Cong Duy Hoang, Philipp Koehn, Gholamreza Haffari, and Trevor Cohn. 2018 · 2018
Later among the works it cites.
Named-entity tagging and domain adaptation for better customized translation
Zhongwei Li, Xuancong Wang, AiTi Aw, Eng Siong Chng, and Haizhou Li. 2018 · 2018
Later among the works it cites.
The Linked Open Data Cloud
John P. McCrae, Andrejs Abele, Paul Buitelaar, Richard Cyganiak, Anja Jentzsch, and Vladimir Andryushechkin. 2018 · 2018
Later among the works it cites.
Machine translation using semantic web technologies: A survey
Diego Moussallem, Matthias Wauer, and Axel-Cyrille Ngonga Ngomo. 2018 · 2018
Later among the works it cites.
Modeling semantics with gated graph neural networks for knowledge base question answering
Daniil Sorokin and Iryna Gurevych. 2018 · 2018
Later among the works it cites.
Open domain question answering using early fusion of knowledge bases and text
Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, and William W Cohen. 2018 · 2018
Later among the works it cites.
Why self-attention? a targeted evaluation of neural machine translation architectures
Gongbo Tang, Mathias Müller, Annette Rios, and Rico Sennrich. 2018b · 2018
Later among the works it cites.
The importance of being recurrent for modeling hierarchical structure
Ke Tran, Arianna Bisazza, and Christof Monz. 2018 · 2018
Later among the works it cites.
Neural machine translation incorporating named entity
Arata Ugawa, Akihiro Tamura, Takashi Ninomiya, Hiroya Takamura, and Manabu Okumura. 2018 · 2018
Later among the works it cites.
Switchout: an efficient data augmentation algorithm for neural machine translation
Xinyi Wang, Hieu Pham, Zihang Dai, and Graham Neubig. 2018 · 2018
Later among the works it cites.