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Defining words in a textual context is a useful task both for practical purposes and for gaining insight into distributed word representations.
Transformer-xl: Attentive language models beyond a fixed-length context
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Distributional semantics and linguistic theory
Gemma Boleda. 2019 · 1905
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Incorporating sememes into chinese definition modeling
Liner Yang, Cunliang Kong, Yun Chen, Yang Liu, Qinan Fan, and Erhong Yang. 2019 · 1905
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Papers in linguistics, 1934-1951
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Wilson Taylor. 1953 · 1953
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Predicting the compositionality of nominal compounds: Giving word embeddings a hard time
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Nltk: The natural language toolkit
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Learning lexical embeddings with syntactic and lexicographic knowledge
Tong Wang, Abdelrahman Mohamed, and Graeme Hirst. 2015 · 2015
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Breaking sticks and ambiguities with adaptive skip-gram
Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, and Dmitry P. Vetrov. 2016 · 2016
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A critique of word similarity as a method for evaluating distributional semantic models
Miroslav Batchkarov, Thomas Kober, Jeremy Reffin, Julie Weeds, and David Weir. 2016 · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016 · 2016
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Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn’t
Anna Gladkova, Aleksandr Drozd, and Satoshi Matsuoka. 2016 · 2016
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Learning distributed representations of sentences from unlabelled data
Felix Hill, Kyunghyun Cho, and Anna Korhonen. 2016 · 2016
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OpenNMT: Open-Source Toolkit for Neural Machine Translation
G. Klein, Y. Kim, Y. Deng, J. Senellart, and A. M. Rush. 2017 · 2017
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil. 2018 · 2018
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Ting-Yun Chang, Ta-Chung Chi, Shang-Chi Tsai, and Yun-Nung Chen. 2018 · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Conditional generators of words definitions
Artyom Gadetsky, Ilya Yakubovskiy, and Dmitry Vetrov. 2018 · 2018
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Distributional models of word meaning
Alessandro Lenci. 2018 · 2018
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Multimodal word meaning induction from minimal exposure to natural text
Angeliki Lazaridou, Marco Arturo Marelli, and Marco Baroni. 2017 · 2017
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Learned in translation: Contextualized word vectors
Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher. 2017 · 2017
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Definition modeling: Learning to define word embeddings in natural language
Thanapon Noraset, Chen Liang, Lawrence Birnbaum, and Doug Downey. 2017 · 2017
Cited alongside, same era.
Dict2vec : Learning word embeddings using lexical dictionaries
Julien Tissier, Christophe Gravier, and Amaury Habrard. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Linear algebraic structure of word senses, with applications to polysemy
Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, and Andrej Risteski. 2018 · 2018
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Auto-encoding dictionary definitions into consistent word embeddings
Tom Bosc and Pascal Vincent. 2018 · 2018
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Scaling neural machine translation
Myle Ott, Sergey Edunov, David Grangier, and Michael Auli. 2018 · 2018
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford. 2018 · 2018
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What are the biases in my word embedding?
Nathaniel Swinger, Maria De-Arteaga, Neil Thomas Heffernan IV, Mark D. M. Leiserson, and Adam Tauman Kalai. 2018 · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 2019
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