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Cosine similarity of contextual embeddings is used in many NLP tasks (e.g., QA, IR, MT) and metrics (e.g., BERTScore).
Improving word representations via global context and multiple word prototypes
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Semantics derived automatically from language corpora contain human-like biases
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Data statements for natural language processing: Toward mitigating system bias and enabling better science
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings
Kawin Ethayarajh. 2019 · 2019
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Putting evaluation in context: Contextual embeddings improve machine translation evaluation
Nitika Mathur, Timothy Baldwin, and Trevor Cohn. 2019 · 2019
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
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Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019 · 2019
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On the sentence embeddings from pre-trained language models
Bohan Li, Hao Zhou, Junxian He, Mingxuan Wang, Yiming Yang, and Lei Li. 2020 · 2020
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BERTScore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2021 · 2021
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BERT has uncommon sense: Similarity ranking for word sense BERTology
Luke Gessler and Nathan Schneider. 2021 · 2021
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Evaluating user perception of speech recognition system quality with semantic distance metric
Suyoun Kim, Duc Le, Weiyi Zheng, Tarun Singh, Abhinav Arora, Xiaoyu Zhai, Christian Fuegen, Ozlem Kalinli, and Michael L. Seltzer. 2021 · 2021
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Nils Reimers and Iryna Gurevych. 2019 · 2019
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MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance
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Moving down the long tail of word sense disambiguation with gloss informed bi-encoders
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Towards understanding linear word analogies
Kawin Ethayarajh, David Duvenaud, and Graeme Hirst. 2019a
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Understanding undesirable word embedding associations
Kawin Ethayarajh, David Duvenaud, and Graeme Hirst. 2019b
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Dynaboard: An evaluation-as-a-service platform for holistic next-generation benchmarking
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All bark and no bite: Rogue dimensions in transformer language models obscure representational quality
William Timkey and Marten van Schijndel. 2021 · 2021
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PromptBERT: Improving BERT sentence embeddings with prompts
Ting Jiang, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Liangjie Zhang, and Qi Zhang. 2022 · 2022
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Richer countries and richer representations
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Factors influencing the surprising instability of word embeddings
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