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1
Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP
In: https://hal.inria.fr/hal-03540069 ; 2022 (2022)
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2
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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3
SIGTYP 2020 Shared Task: Prediction of Typological Features ...
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4
Linguistic calibration through metacognition: aligning dialogue agent responses with expected correctness ...
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5
Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset ...
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6
Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model ...
Mielke, Sabrina J.; Eisner, Jason. - : arXiv, 2018
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Are All Languages Equally Hard to Language-Model? ...
Abstract: For general modeling methods applied to diverse languages, a natural question is: how well should we expect our models to work on languages with differing typological profiles? In this work, we develop an evaluation framework for fair cross-linguistic comparison of language models, using translated text so that all models are asked to predict approximately the same information. We then conduct a study on 21 languages, demonstrating that in some languages, the textual expression of the information is harder to predict with both $n$-gram and LSTM language models. We show complex inflectional morphology to be a cause of performance differences among languages. ... : Published at NAACL 2018 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1806.03743
https://dx.doi.org/10.48550/arxiv.1806.03743
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8
Unsupervised Disambiguation of Syncretism in Inflected Lexicons ...
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