@inproceedings{cho-etal-2023-sir,
    title = "{SIR}-{ABSC}: Incorporating Syntax into {R}o{BERT}a-based Sentiment Analysis Models with a Special Aggregator Token",
    author = "Cho, Ikhyun  and
      Jung, Yoonhwa  and
      Hockenmaier, Julia",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.findings-emnlp.572/",
    doi = "10.18653/v1/2023.findings-emnlp.572",
    pages = "8535--8550",
    abstract = "We present a simple, but effective method to incorporate syntactic dependency information directly into transformer-based language models (e.g. RoBERTa) for tasks such as Aspect-Based Sentiment Classification (ABSC), where the desired output depends on specific input tokens. In contrast to prior approaches to ABSC that capture syntax by combining language models with graph neural networks over dependency trees, our model, Syntax-Integrated RoBERTa for ABSC (SIR-ABSC) incorporates syntax directly into the language model by using a novel aggregator token. Yet, SIR-ABSC outperforms these more complex models, yielding new state-of-the-art results on ABSC."
}