@inproceedings{son-etal-2022-discourse,
    title = "Discourse Relation Embeddings: Representing the Relations between Discourse Segments in Social Media",
    author = "Son, Youngseo  and
      Varadarajan, Vasudha  and
      Schwartz, H. Andrew",
    editor = "Han, Wenjuan  and
      Zheng, Zilong  and
      Lin, Zhouhan  and
      Jin, Lifeng  and
      Shen, Yikang  and
      Kim, Yoon  and
      Tu, Kewei",
    booktitle = "Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.umios-1.5/",
    doi = "10.18653/v1/2022.umios-1.5",
    pages = "45--55",
    abstract = "Discourse relations are typically modeled as a discrete class that characterizes the relation between segments of text (e.g. causal explanations, expansions). However, such predefined discrete classes limit the universe of potential relationships and their nuanced differences. Adding higher-level semantic structure to contextual word embeddings, we propose representing discourse relations as points in high dimensional continuous space. However, unlike words, discourse relations often have no surface form (relations are in between two segments, often with no word or phrase in that gap) which presents a challenge for existing embedding techniques. We present a novel method for automatically creating discourse relation embeddings (DiscRE), addressing the embedding challenge through a weakly supervised, multitask approach to learn diverse and nuanced relations in social media. Results show DiscRE representations obtain the best performance on Twitter discourse relation classification (macro F1=0.76), social media causality prediction (from F1=0.79 to 0.81), and perform beyond modern sentence and word transformers at traditional discourse relation classification, capturing novel nuanced relations (e.g. relations at the intersection of causal explanations and counterfactuals)."
}