@inproceedings{nuo-guo-2024-hybrid,
    title = "Hybrid of Spans and Table-Filling for Aspect-Level Sentiment Triplet Extraction",
    author = "Nuo, Minghua  and
      Guo, Chaofan",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.742",
    pages = "8464--8473",
    abstract = "Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research. Recently, researchers have proposed different tagging schemes, containing tagging of words, tagging of word pairs, and tagging of spans. However, the first two of these methods are often insufficient for the identification of multi-word terms, while the span tagging can label the entire phrase span, but it lacks the interactive information between words. In this paper, we propose Span in Table(S{\&}T) model which combining span with table-filling. Specifically, S{\&}T model achieve full fusion of syntactic and contextual features through cross-attention and generate the structures of word-pair table through Biaffine. Then, our model converts it to a span table by computing semantic distance based on syntactic dependency tree, which can enrich each unit of span table with semantic and interactive information. Meanwhile, the initial sentence features are constructed as simple phrase tables to enhance textual information of the phrase itself. In decoding, we define 8 types of labels for identifying three dimensions including aspect, opinion, and sentiment. Finally, the extensive experiments on D2 dataset show S{\&}T model achieves competitive results in ASTE task, the results certify the effectiveness and robustness of our S{\&}T model.",
}

Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research. Recently, researchers have proposed different tagging schemes, containing tagging of words, tagging of word pairs, and tagging of spans. However, the first two of these methods are often insufficient for the identification of multi-word terms, while the span tagging can label the entire phrase span, but it lacks the interactive information between words. In this paper, we propose Span in Table(S{\&}T) model which combining span with table-filling. Specifically, S{\&}T model achieve full fusion of syntactic and contextual features through cross-attention and generate the structures of word-pair table through Biaffine. Then, our model converts it to a span table by computing semantic distance based on syntactic dependency tree, which can enrich each unit of span table with semantic and interactive information. Meanwhile, the initial sentence features are constructed as simple phrase tables to enhance textual information of the phrase itself. In decoding, we define 8 types of labels for identifying three dimensions including aspect, opinion, and sentiment. Finally, the extensive experiments on D2 dataset show S{\&}T model achieves competitive results in ASTE task, the results certify the effectiveness and robustness of our S{\&}T model.
Nuo, Minghua Guo, Chaofan
Hybrid of Spans and Table-Filling for Aspect-Level Sentiment Triplet Extraction