@inproceedings{tan-bansal-2020-vokenization,
    title = "Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision",
    author = "Tan, Hao  and
      Bansal, Mohit",
    editor = "Webber, Bonnie  and
      Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.emnlp-main.162/",
    doi = "10.18653/v1/2020.emnlp-main.162",
    pages = "2066--2080",
    abstract = "Humans learn language by listening, speaking, writing, reading, and also, via interaction with the multimodal real world. Existing language pre-training frameworks show the effectiveness of text-only self-supervision while we explore the idea of a visually-supervised language model in this paper. We find that the main reason hindering this exploration is the large divergence in magnitude and distributions between the visually-grounded language datasets and pure-language corpora. Therefore, we develop a technique named {\textquotedblleft}vokenization{\textquotedblright} that extrapolates multimodal alignments to language-only data by contextually mapping language tokens to their related images (which we call {\textquotedblleft}vokens{\textquotedblright}). The {\textquotedblleft}vokenizer{\textquotedblright} is trained on relatively small image captioning datasets and we then apply it to generate vokens for large language corpora. Trained with these contextually generated vokens, our visually-supervised language models show consistent improvements over self-supervised alternatives on multiple pure-language tasks such as GLUE, SQuAD, and SWAG."
}