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Past vocabulary learning techniques identify the relevant vocabulary prior to training based on statistical and entropy-based assumptions that largely neglect the role of model training. Empirically, we observe that the trained translation models are induced to use a byte-pair encoding (BPE) vocabulary subset distinct from the original BPE vocabulary while leading to performance improvements upon retraining with the induced vocabulary. In this paper, we first analyze this difference in the context of neural machine translation by examining the vocabulary and entropy shifts during self-training -- where each iteration creates a labeled dataset by pairing source sentences with the model&#39;s predictions to define a new vocabulary. Based on our findings, we propose *self-vocabularizing training*, which iteratively self-selects vocabulary that is both smaller in size and more optimal with up to 1.49 BLEU improvement. Moreover, we found that using deeper model architectures leads to both an increase in the use of unique tokens and a significant reduction in the vocabulary size by 6-8%.

NAACL 2025

Self-Vocabularizing Training for Neural Machine Translation

self-vocabularizing training

vocabulary learning

neural machine translation

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Past vocabulary learning techniques identify the relevant vocabulary prior to training based on statistical and entropy-based assumptions that largely neglect the role of model training. Empirically, we observe that the trained translation models are induced to use a byte-pair encoding (BPE) vocabulary subset distinct from the original BPE vocabulary while leading to performance improvements upon retraining with the induced vocabulary. In this paper, we first analyze this difference in the context of neural machine translation by examining the vocabulary and entropy shifts during self-training -- where each iteration creates a labeled dataset by pairing source sentences with the model's predictions to define a new vocabulary. Based on our findings, we propose *self-vocabularizing training*, which iteratively self-selects vocabulary that is both smaller in size and more optimal with up to 1.49 BLEU improvement. Moreover, we found that using deeper model architectures leads to both an increase in the use of unique tokens and a significant reduction in the vocabulary size by 6-8%.

poster

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

We challenge the prevailing assumption that LLMs must rely fully on sub-word tokens for high-quality text generation. To this end, we propose the ``Generative Pretrained Thoughtformer'' (GPTHF), a hierarchical transformer language model capable of text generation by compressing text into sentence embeddings and employing a sentence attention mechanism. GPTHF retains GPT’s architecture, modifying only token interactions via dynamic sparse attention masks. 


Our experiments show that GPTHF achieves an up to an order of magnitude improvement in FLOPs efficiency and a threefold increase in runtime speed compared to equally-sized GPT models in the low-size regime. This is achieved through a unique generation method that caches and reuses sentence embeddings, allowing significant portions of the input to bypass large parts of the network.

Text Compression for Efficient Language Generation

When developing language technology, researchers have routinely turned to transfer learning to resolve the data scarcity conundrum presented in low-resource languages. As far as we know, this study is the first to evaluate the amount of documentation needed for transfer learning, specifically the smallest vocabulary size needed to create a sentence embedding space. In adopting widely spoken languages as a proxy for low-resource languages, our experiments show that the relationship between a sentence embedding's vocabulary size and performance is logarithmic with performance leveling at a vocabulary size of 25,000. It should be noted that this relationship cannot be replicated across all languages and this level of documentation does not exist for many low-resource languages. We do observe, however, that performance accelerates at a vocabulary size of <= 1000, a quantity that is present in most low-resource language documentation. These results can aid researchers in understanding whether a low-resource language has enough documentation necessary to support the creation of a sentence embedding and language model.

How many words does it take to understand a low-resource language?

A two-part affine approximation has been found to be a good approximation for transformer computations over certain subject-object relations. Adapting the Bigger Analogy Test Set, we show that the linear transformation W s , where s is a middle-layer representation of a subject token and W is derived from model derivatives, can accurately reproduce final object states for many relations. This linear technique achieves 90% faithfulness on morphological relations, with similar findings across languages and models. Our results suggest that some conceptual relationships in language models, such as morphology, are readily interpretable from latent space and are sparsely encoded by cross-layer linear transformations.

Linear Relational Decoding of Morphology in Language Models

Accurate detection of offensive language is essential for a number of applications related to social media safety. There is a sharp contrast in performance in this task between low- and high-resource languages. In this paper, we adapt fine-tuning strategies for the downstream task of offensive language detection to a previously unexplored low-resourced language, Sinhala. Using this approach, we introduce four models: ''Subasa-XLM-R'', which incorporates an intermediate Pre-Finetuning step using Masked Rationale Prediction. Two variants of ''Subasa-Llama'' and ''Subasa-Mistral'', are fine-tuned versions of Llama and Mistral, respectively, with a task-specific strategy. We evaluate our models on the SOLD benchmark dataset for Sinhala offensive language detection. All our models outperform existing baselines. Subasa-XLM-R achieves the highest Macro F1 score (0.84) surpassing state-of-the-art Large Language Models (LLMs) like GPT-4o when evaluated on the same SOLD benchmark dataset under zero-shot settings. The models and code are publicly available.

Subasa - Adapting Language Models for Low-resourced Offensive Language Detection in Sinhala

The frontier of large language model (LLM) development has largely been substantiated by knowledge-intensive tasks specified in English. In this proposed thesis, I argue for the key role that multilinguality occupies in the development of practical and knowledgeable LLMs. Importantly, I show that informed use of multilinguality enhances LLMs' capabilities, and our understanding thereof.


First, I consider practical methods to improve LLM's performance on standard natural language processing (NLP) tasks by leveraging their existing multilingual knowledge. Then, I investigate the underlying multilingual knowledge of LLMs with two benchmarks: on complex reasoning, and on territorial disputes. These benchmarks reveal LLMs' inconsistent performance across languages. I then design efficient techniques, both at inference-time and training-time, to address these discrepancies. I close with a proposed work extending the geopolitical benchmark to the retrieved augmented generation scenario.

Towards Practical and Knowledgeable Large Language Models for a Multilingual World

How does textual representation of audio relate to the Large Language Model's (LLMs) learning about the audio world? This research investigates the extent to which LLMs can be prompted to generate audio, despite their primary training in textual data. We employ a three-tier approach, progressively increasing the complexity of audio generation: 1) Musical Notes, 2) Environmental Sounds, and 3) Human Speech. To bridge the gap between text and audio, we leverage code as an intermediary, prompting LLMs to generate code that, when executed, produces the desired audio output. To evaluate the quality and accuracy of the generated audio, we employ FAD and CLAP scores. Our findings reveal that while LLMs can generate basic audio features, their performance deteriorates as the complexity of the audio increases. This suggests that while LLMs possess a latent understanding of the auditory world, their ability to translate this understanding into tangible audio output remains rudimentary. Further research into techniques that can enhance the quality and diversity of LLM-generated audio can lead to an improvement in the performance of text-based LLMs in generating audio.

Probing Audio-Generation Capabilities of Text-Based Language Models

The ways in which natural language processing (NLP) can inform how veterans can improve effectiveness in translating military experience to workforce utility is underexplored. We design NLP experiments to evaluate the degree of explanation in veteran job interview responses as a proxy for perceived hireability. We examine linguistic and psycholinguistic features, context, and participant variability to investigate the mechanics of effective communication in employee selection. Results yield good performance when distinguishing between varying degrees of explanation in responses using LIWC features, indicating robustness of linguistic feature integration. Classifying Over- and Under-explained responses reflects challenges of class imbalance and the limitations of tested NLP methods for detecting subtleties in overly verbose or concise communication. Our findings have immediate applications for assistive technologies in job interview settings, and broader implications for enhancing automated communication assessment tools and refining strategies for training and interventions in communication-heavy fields.

Linguistic Analysis of Veteran Job Interviews to Assess Effectiveness in Translating Military Expertise to the Civilian Workforce

This paper introduces a new dataset for classifying memes by their template and communicative intent. It includes a broad selection of meme templates and examples scraped from imgflip and a smaller hand-annotated set of memes scraped from Reddit. The Reddit memes have been annotated for meta-category
using a novel annotation scheme that classifies memes by the structure of the perspective they are being used to communicate. YOLOv11 and ChatGPT 4o are used to provide baseline modeling results. We find that YOLO struggles with template classification on real-world data but outperforms ChatGPT in classifying meta-categories.

MetaMeme: A Dataset for Meme Template and Meta-Category Classification

Content moderation is essential in preventing the spread of harmful content on the Internet. However, there are instances where moderation fails and it is important to understand when and why that happens. Workflows that aim to uncover a system's weakness typically use clustering of the data points' embeddings to group errors together. In this paper, we evaluate the K-Means clustering of four text representations for the task of offensive language detection in English and Levantine Arabic. We find Sentence-BERT (S-BERT) embeddings give the most human-interpretable clustering for English errors and the grouping is mainly based on the targeted group in the text. Meanwhile, S-BERT embeddings of Large Language Model (LLM)-generated linguistic features give the most interpretable clustering for Arabic errors.

Representing and Clustering Errors in Offensive Language Detection

Cutting-edge LLMs have emerged as powerful tools for multilingual communication and understanding. However, LLMs perform worse in Common Sense Reasoning (CSR) tasks when prompted in low-resource languages (LRLs) like Hindi or Swahili compared to high-resource languages (HRLs) like English. Equalizing this inconsistent access to quality LLM outputs is crucial to ensure fairness for speakers of LRLs and across diverse linguistic communities. In this paper, we propose an approach to bridge this gap in LLM performance. Our approach involves fine-tuning an LLM on synthetic code-switched text generated using controlled language-mixing methods. We empirically demonstrate that fine-tuning LLMs on synthetic code-switched datasets leads to substantial improvements in LRL model performance while preserving or enhancing performance in HRLs. Additionally, we present a new dataset of synthetic code-switched text derived from the CommonSenseQA dataset, featuring three distinct language ratio configurations.

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