---
title: "Who cites Finding the Optimal Vocabulary Size for Neural…"
description: "54 papers of the Elamur Research library cite «Finding the Optimal Vocabulary Size for Neural Machine Translation», found in their bibliographies; the…"
canonical: "https://research.elamur.ai/free/en/papers/gowda-2020-finding-optimal-vocabulary-size-neural/cited-by/"
---
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# Who cites «Finding the Optimal Vocabulary Size for Neural Machine Translation»

54 papers of our library cite it (of the 140,884 whose bibliographies we read) · 4 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/gowda-2020-finding-optimal-vocabulary-size-neural/)

1. [**Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP**Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky, et al.*2021 · 89 citations*](https://md.research.elamur.ai/free/en/papers/mielke-2021-between-words-characters-brief-history/)
2. [**Getting the most out of your tokenizer for pre-training and domain adaptation**Gautier Dagan, Gabriel Synnaeve, Baptiste Roziere*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/dagan-2024-getting-most-out-tokenizer-pre/)
3. [**Optimizing the Size of Subword Vocabularies in Dialect Classification**Vani Kanjirangat, Tanja Samardžić, Ljiljana Dolamic, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/kanjirangat-2023-optimizing-size-subword-vocabularies-dialect/)
4. [**Bringing Mapudungun into the Modern MT Ecosystem: Morphology-Aware Tokenization for NLLB-200 Fine-Tuning**Isaac Thompson, Brandon Rogers, Eric Ringger*2026*](https://md.research.elamur.ai/free/en/papers/thompson-2026-bringing-mapudungun-into-modern-mt/)

50 more papers of our library cite it; they have no page here yet.

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