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Natural language definitions possess a recursive, self-explanatory semantic structure that can support representation learning methods able to preserve explicit conceptual relations and constraints in the latent space.
Roberta: A robustly optimized bert pretraining approach
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Felix Hill, Roi Reichart, and Anna Korhonen. 2015 · 2015
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Problems with evaluation of word embeddings using word similarity tasks
Manaal Faruqui, Yulia Tsvetkov, Pushpendre Rastogi, and Chris Dyer. 2016 · 2016
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Knowledge graph embedding by flexible translation
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Daniela Gerz, Ivan Vulić, Felix Hill, Roi Reichart, and Anna Korhonen. 2016 · 2016
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Learning to understand phrases by embedding the dictionary
Felix Hill, Kyunghyun Cho, Anna Korhonen, and Yoshua Bengio. 2016 · 2016
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Categorization of semantic roles for dictionary definitions
Vivian Silva, Siegfried Handschuh, and André Freitas. 2016 · 2016
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Building lexical vector representations from concept definitions
Danilo Silva de Carvalho and Minh Le Nguyen. 2017 · 2017
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Poincaré embeddings for learning hierarchical representations
Maximillian Nickel and Douwe Kiela. 2017 · 2017
Multi-relational poincaré graph embeddings
Ivana Balazevic, Carl Allen, and Timothy Hospedales. 2019 · 2019
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Language modelling makes sense: Propagating representations through wordnet for full-coverage word sense disambiguation
Daniel Loureiro and Alipio Jorge. 2019 · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Exploring knowledge graphs in an interpretable composite approach for text entailment
Vivian S Silva, André Freitas, and Siegfried Handschuh. 2019 · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Definition modeling: Learning to define word embeddings in natural language
Thanapon Noraset, Chen Liang, Larry Birnbaum, and Doug Downey. 2017 · 2017
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Dict2vec: Learning word embeddings using lexical dictionaries
Julien Tissier, Christophe Gravier, and Amaury Habrard. 2017 · 2017
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Auto-encoding dictionary definitions into consistent word embeddings
Tom Bosc and Pascal Vincent. 2018 · 2018
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Conditional generators of words definitions
Artyom Gadetsky, Ilya Yakubovskiy, and Dmitry Vetrov. 2018 · 2018
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Hyperbolic neural networks
Octavian Ganea, Gary Bécigneul, and Thomas Hofmann. 2018 · 2018
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Skip-gram word embeddings in hyperbolic space
Matthias Leimeister and Benjamin J Wilson. 2018 · 2018
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Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Drg2vec: Learning word representations from definition relational graph
Xiaobo Shu, Bowen Yu, Zhenyu Zhang, and Tingwen Liu. 2020 · 2020
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Mpnet: Masked and permuted pre-training for language understanding
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2020 · 2020
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Manifold learning-based word representation refinement incorporating global and local information
Wenyu Zhao, Dong Zhou, Lin Li, and Jinjun Chen. 2020 · 2020
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Defsent: Sentence embeddings using definition sentences
Hayato Tsukagoshi, Ryohei Sasano, and Koichi Takeda. 2021 · 2021
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Semeval-2022 task 1: Codwoe–comparing dictionaries and word embeddings
Timothee Mickus, Kees van Deemter, Mathieu Constant, and Denis Paperno. 2022 · 2022
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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, Ji Ma, Keith Hall, Daniel Cer, and Yinfei Yang. 2022 · 2022
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Hg2vec: Improved word embeddings from dictionary and thesaurus based heterogeneous graph
Qitong Wang and Mohammed J Zaki. 2022 · 2022
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Learning disentangled representations for natural language definitions
Danilo S Carvalho, Giangiacomo Mercatali, Yingji Zhang, and Andre Freitas. 2023 · 2023
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