@inproceedings{chen-qian-2020-relation,
    title = "Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis",
    author = "Chen, Zhuang  and
      Qian, Tieyun",
    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.340/",
    doi = "10.18653/v1/2020.acl-main.340",
    pages = "3685--3694",
    abstract = "Aspect-based sentiment analysis (ABSA) involves three subtasks, i.e., aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Most existing studies focused on one of these subtasks only. Several recent researches made successful attempts to solve the complete ABSA problem with a unified framework. However, the interactive relations among three subtasks are still under-exploited. We argue that such relations encode collaborative signals between different subtasks. For example, when the opinion term is \textit{{\textquotedblleft}delicious{\textquotedblright}}, the aspect term must be \textit{{\textquotedblleft}food{\textquotedblright}} rather than \textit{{\textquotedblleft}place{\textquotedblright}}. In order to fully exploit these relations, we propose a Relation-Aware Collaborative Learning (RACL) framework which allows the subtasks to work coordinately via the multi-task learning and relation propagation mechanisms in a stacked multi-layer network. Extensive experiments on three real-world datasets demonstrate that RACL significantly outperforms the state-of-the-art methods for the complete ABSA task."
}