@inproceedings{zhang-etal-2024-unveiling,
    title = "Unveiling Semantic Information in Sentence Embeddings",
    author = "Zhang, Leixin  and
      Burian, David  and
      John, Vojt{\v{e}}ch  and
      Bojar, Ond{\v{r}}ej",
    editor = "Bonial, Claire  and
      Bonn, Julia  and
      Hwang, Jena D.",
    booktitle = "Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.dmr-1.5",
    pages = "39--47",
    abstract = "This study evaluates the extent to which semantic information is preserved within sentence embeddings generated from state-of-art sentence embedding models: SBERT and LaBSE. Specifically, we analyzed 13 semantic attributes in sentence embeddings. Our findings indicate that some semantic features (such as tense-related classes) can be decoded from the representation of sentence embeddings. Additionally, we discover the limitation of the current sentence embedding models: inferring meaning beyond the lexical level has proven to be difficult.",
}

This study evaluates the extent to which semantic information is preserved within sentence embeddings generated from state-of-art sentence embedding models: SBERT and LaBSE. Specifically, we analyzed 13 semantic attributes in sentence embeddings. Our findings indicate that some semantic features (such as tense-related classes) can be decoded from the representation of sentence embeddings. Additionally, we discover the limitation of the current sentence embedding models: inferring meaning beyond the lexical level has proven to be difficult.
Zhang, Leixin Burian, David John, Vojt{\v{e}}ch Bojar, Ond{\v{r}}ej
Unveiling Semantic Information in Sentence Embeddings