@inproceedings{peinelt-etal-2020-tbert,
    title = "t{BERT}: Topic Models and {BERT} Joining Forces for Semantic Similarity Detection",
    author = "Peinelt, Nicole  and
      Nguyen, Dong  and
      Liakata, Maria",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.acl-main.630/",
    doi = "10.18653/v1/2020.acl-main.630",
    pages = "7047--7055",
    abstract = "Semantic similarity detection is a fundamental task in natural language understanding. Adding topic information has been useful for previous feature-engineered semantic similarity models as well as neural models for other tasks. There is currently no standard way of combining topics with pretrained contextual representations such as BERT. We propose a novel topic-informed BERT-based architecture for pairwise semantic similarity detection and show that our model improves performance over strong neural baselines across a variety of English language datasets. We find that the addition of topics to BERT helps particularly with resolving domain-specific cases."
}