Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary?
Adam Nohejl, Frederikus Hudi, Eunike Andriani Kardinata, Shintaro Ozaki, Maria Angelica Riera Machin, Hongyu Sun, Justin Vasselli, Taro Watanabe
TL;DR
This work introduces TUBELEX, a multilingual corpus of untranslated YouTube subtitles to approximate spoken vocabulary frequencies. By processing and anonymizing subtitles across Chinese, English, Indonesian, Japanese, and Spanish, the authors produce frequency norms that show strong correlations with lexical decision time, word familiarity, and lexical complexity, often outperforming traditional film-subtitle resources. They demonstrate that simple frequency-based models can surpass several advanced systems in predicting lexical complexity for English and Japanese, and they provide freely available language models and embeddings. The approach broadens access to robust lexical norms for languages with limited existing resources and highlights opportunities to extend beyond frequency through dispersion metrics, context, and source integration. Overall, TUBELEX offers a scalable, multilingual alternative to film subtitles for modeling spoken vocabulary with practical implications for readability, language teaching, and NLP systems."
Abstract
Word frequency is a key variable in psycholinguistics, useful for modeling human familiarity with words even in the era of large language models (LLMs). Frequency in film subtitles has proved to be a particularly good approximation of everyday language exposure. For many languages, however, film subtitles are not easily available, or are overwhelmingly translated from English. We demonstrate that frequencies extracted from carefully processed YouTube subtitles provide an approximation comparable to, and often better than, the best currently available resources. Moreover, they are available for languages for which a high-quality subtitle or speech corpus does not exist. We use YouTube subtitles to construct frequency norms for five diverse languages, Chinese, English, Indonesian, Japanese, and Spanish, and evaluate their correlation with lexical decision time, word familiarity, and lexical complexity. In addition to being strongly correlated with two psycholinguistic variables, a simple linear regression on the new frequencies achieves a new high score on a lexical complexity prediction task in English and Japanese, surpassing both models trained on film subtitle frequencies and the LLM GPT-4. Our code, the frequency lists, fastText word embeddings, and statistical language models are freely available at https://github.com/naist-nlp/tubelex.