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The performance of Large Language Models (LLMs) degrades from the temporal drift between data used for model training and newer text seen during inference.
Federated learning of out-of-vocabulary words
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Neural machine translation with byte-level subwords
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Bleu: a method for automatic evaluation of machine translation
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Emmanuel Cartier. 2017 · 2017
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Few-shot representation learning for out-of-vocabulary words
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 2019
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Identification of adjective-noun neologisms using pretrained language models
John Philip McCrae. 2019 · 2019
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Unsupervised neologism normalization using embedding space mapping
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Domain adaptation challenges of BERT in tokenization and sub-word representations of out-of-vocabulary words
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NYTWIT: A dataset of novel words in the New York Times
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Multi-level out-of-vocabulary words handling approach
Johannes V. Lochter, Renato M. Silva, and Tiago A. Almeida. 2022 · 2022
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Timelms: Diachronic language models from twitter
Daniel Loureiro, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, and Jose Camacho-Collados. 2022 · 2022
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Time waits for no one! analysis and challenges of temporal misalignment
Kelvin Luu, Daniel Khashabi, Suchin Gururangan, Karishma Mandyam, and Noah A. Smith. 2022 · 2022
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Entity cloze by date: What LMs know about unseen entities
Yasumasa Onoe, Michael Zhang, Eunsol Choi, and Greg Durrett. 2022 · 2022
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Comet: A neural framework for mt evaluation
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020 · 2020
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Where new words are born: Distributional semantic analysis of neologisms and their semantic neighborhoods
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Dynamic benchmarking of masked language models on temporal concept drift with multiple views
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