@inproceedings{singh-etal-2021-end,
    title = "An End-to-End Network for Emotion-Cause Pair Extraction",
    author = "Singh, Aaditya  and
      Hingane, Shreeshail  and
      Wani, Saim  and
      Modi, Ashutosh",
    editor = "De Clercq, Orphee  and
      Balahur, Alexandra  and
      Sedoc, Joao  and
      Barriere, Valentin  and
      Tafreshi, Shabnam  and
      Buechel, Sven  and
      Hoste, Veronique",
    booktitle = "Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.wassa-1.9/",
    pages = "84--91",
    abstract = "The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential clause-pairs of emotions and their corresponding causes in a document. Unlike the more well-studied task of Emotion Cause Extraction (ECE), ECPE does not require the emotion clauses to be provided as annotations. Previous works on ECPE have either followed a multi-stage approach where emotion extraction, cause extraction, and pairing are done independently or use complex architectures to resolve its limitations. In this paper, we propose an end-to-end model for the ECPE task. Due to the unavailability of an English language ECPE corpus, we adapt the NTCIR-13 ECE corpus and establish a baseline for the ECPE task on this dataset. On this dataset, the proposed method produces significant performance improvements ({\ensuremath{\sim}} 6.5{\%} increase in F1 score) over the multi-stage approach and achieves comparable performance to the state-of-the-art methods."
}