@inproceedings{nazir-sadrzadeh-2024-adjective,
    title = "How Does an Adjective Sound Like? Exploring Audio Phrase Composition with Textual Embeddings",
    author = "Nazir, Saba  and
      Sadrzadeh, Mehrnoosh",
    editor = "Qiu, Amy  and
      Noble, Bill  and
      Pagmar, David  and
      Maraev, Vladislav  and
      Ilinykh, Nikolai",
    booktitle = "Proceedings of the 2024 CLASP Conference on Multimodality and Interaction in Language Learning",
    month = oct,
    year = "2024",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.clasp-1.3/",
    pages = "13--18",
    abstract = "We learn matrix representations for the fre- quent sound-relevant adjectives of English and compose them with vector representations of their nouns. The matrices are learnt jointly from audio and textual data, via linear regres- sion and tensor skipgram. They are assessed using an adjective similarity benchmark and also a novel adjective-noun phrase similarity dataset, applied to two tasks: semantic similar- ity and audio similarity. Joint learning via Ten- sor Skipgram (TSG) outperforms audio-only models, matrix composition outperforms addi- tion and non compositional phrase vectors."
}