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20152024
most citedBetween words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP

106 citations · 160 across the 17 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

cs.CL2021★ 106 cited

Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP

Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky +8

What are the units of text that we want to model? From bytes to multi-word expressions, text can be analyzed and generated at many granularities. Until recently, most natural langu…

cs.CL2021★ 8 cited

Unsupervised and Distributional Detection of Machine-Generated Text

Matthias Gallé, Jos Rozen, Germán Kruszewski +1

The power of natural language generation models has provoked a flurry of interest in automatic methods to detect if a piece of text is human or machine-authored. The problem so far…

cs.CL2021

Speeding Up Entmax

Maxat Tezekbayev, Vassilina Nikoulina, Matthias Gallé +1

Softmax is the de facto standard in modern neural networks for language processing when it comes to normalizing logits. However, by producing a dense probability distribution each…

cs.CL2021

Multilingual Unsupervised Neural Machine Translation with Denoising Adapters

Ahmet Üstün, Alexandre Bérard, Laurent Besacier +1

We consider the problem of multilingual unsupervised machine translation, translating to and from languages that only have monolingual data by using auxiliary parallel language pai…

cs.CL2021★ 10 cited

On the Evaluation of Machine Translation for Terminology Consistency

Md Mahfuz ibn Alam, Antonios Anastasopoulos, Laurent Besacier +4

As neural machine translation (NMT) systems become an important part of professional translator pipelines, a growing body of work focuses on combining NMT with terminologies. In ma…

cs.CL2021★ 1 cited

Breaking Writer's Block: Low-cost Fine-tuning of Natural Language Generation Models

Alexandre Duval, Thomas Lamson, Gael de Leseleuc de Kerouara +1

It is standard procedure these days to solve Information Extraction task by fine-tuning large pre-trained language models. This is not the case for generation task, which relies on…

cs.CL2021★ 4 cited

The Rediscovery Hypothesis: Language Models Need to Meet Linguistics

Vassilina Nikoulina, Maxat Tezekbayev, Nuradil Kozhakhmet +3

There is an ongoing debate in the NLP community whether modern language models contain linguistic knowledge, recovered through so-called probes. In this paper, we study whether lin…