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Author(s)YearTitleLink to paperCommentsPresented
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Vaswani et al.2017"Attention is all you need"https://arxiv.org/abs/1706.03762Paper introducing the Transformer architecture14/02/2018
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Dai et al.2019"Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context"https://arxiv.org/abs/1901.02860N/A
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Voita et al.2018"Context-Aware Neural Machine Translation Learns Anaphora Resolution"http://www.aclweb.org/anthology/P18-1117Analysis of the capacities of a Transformer architectureN/A
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Devlin et al.2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandinghttps://arxiv.org/abs/1810.04805BERT paper14/02/2019
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Peters et al.2018Deep contextualized word representationshttps://arxiv.org/pdf/1802.05365.pdfELMo PaperN/A
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Lakretz & al2019The emergence of number and syntax units in LSTM language modelshttps://arxiv.org/abs/1903.07435N/A
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Lample & al2017Unsupervised Machine Translation Using Monolingual Corpora Onlyhttps://arxiv.org/abs/1711.00043N/A
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Shaw & al2018Self-Attention with Relative Position Representationshttps://arxiv.org/abs/1803.02155N/A
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Yin & Shen2018On the Dimensionality of Word Embeddinghttps://arxiv.org/abs/1812.04224N/A
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Cer & al2018Universal Sentence Encoderhttps://arxiv.org/abs/1803.11175
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Logeswaran & Lee2018an efficient framework for learning sentence representationshttps://arxiv.org/abs/1803.02893QuickThoughts
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Kiros & al2015Skip-Thought Vectorshttps://arxiv.org/abs/1506.06726SkipThoughts
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Conneau & al2018Supervised Learning of Universal Sentence Representations from Natural Language Inference Datahttps://arxiv.org/abs/1705.02364Infersent
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Bowman & al2018Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modelinghttps://arxiv.org/abs/1812.10860
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