Challenge: Using a pre-defined vocabulary is a common approach to selecting text inputs . however, using a large vocabulary is not economical, as it limits the model's applicability on computation-or memoryconstrained scenarios.
Approach: They propose a more sophisticated variational vocabulary dropout to perform vocabulary selection . they propose two new metrics to measure area under accuracy-vocab curve and Vocab Size under X% accuracy drop .
Outcome: The proposed framework outperforms the baselines on the vocabulary selection problem on multiple NLP classification tasks.

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Large Vocabulary Size Improves Large Language Models (2025.findings-acl)

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Challenge: Existing studies have investigated the properties of internal layers in large language models, but no studies have defined the vocabulary size.
Approach: They propose a method to use a new vocabulary instead of the pre-defined one in a continual training scenario.
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The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation (2022.naacl-main)

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Challenge: Neural Machine Translation models can be optimized to improve latency by constraining the set of output words . lexical shortlisting fails to select the right set of input words for semantically non-compositional phenomena such as idiomatic expressions.
Approach: They propose a model of vocabulary selection that constrains the set of allowed output words . they propose to increase the size of the allowed set to restore translation quality .
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Finding the Optimal Vocabulary Size for Neural Machine Translation (2020.findings-emnlp)

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Challenge: Class imbalance is said to exist when one or more classes are not of approximately equal frequency in data.
Approach: They cast neural machine translation (NMT) as a classification task in an autoregressive setting and examine its limitations.
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VocabTailor: Dynamic Vocabulary Selection for Downstream Tasks in Small Language Models (2026.findings-acl)

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Challenge: Existing static vocabulary pruning designs that reduce memory usage suffer from rigid, one-size-fits-all designs that cause information loss during the prefill stage and lack flexibility.
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Rethinking Vocabulary Augmentation: Addressing the Challenges of Low-Resource Languages in Multilingual Models (2025.coling-main)

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Challenge: Existing methods to augment vocabularies ignore the disparities between model representation and frequency distributions.
Approach: They propose an Entropy-Consistency Word Selection method which integrates semantic and frequency metrics for vocabulary augmentation.
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Text Classification with Few Examples using Controlled Generalization (N19-1)

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Challenge: Current training data for text classification is limited, resulting in limited generalization capacity.
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Outcome: The proposed approach is especially effective in low-data scenarios compared to state-of-the-art methods.
Vocabulary Learning via Optimal Transport for Neural Machine Translation (2021.acl-long)

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Challenge: Empirical results show that VOLT beats widely-used vocabularies in diverse scenarios, including WMT-14 English-German translation, TED bilingual translation, and TED multilingual translation.
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Improving Lexical Choice in Neural Machine Translation (N18-1)

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Challenge: False positives: the output layer rewards frequent words disproportionately, we argue . Falsibles: a model that learns word representations in continuous space tends to translate rare words .
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Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification (2021.naacl-main)

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Challenge: Many text classification algorithms depend on the size of the corpus’ vocabulary due to their bag-of-words representation.
Approach: They propose to evaluate how preprocessing techniques affect the run-time of models by evaluating ten techniques over four models and two datasets.
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Efficient Vocabulary Reduction for Small Language Models (2025.coling-industry)

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Challenge: Large language models (LLMs) have high computational costs and energy consumption, making their deployment in industrial settings difficult.
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