@inproceedings{zhang-etal-2025-eng,
    title = "{ENG}-{DRB}: {PDTB}-style Discourse Relation Bank on Engineering Tutorial Video Scripts",
    author = "Zhang, Cheng  and
      Kakarla, Rajasekhar  and
      Wei, Kangda  and
      Huang, Ruihong",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.findings-ijcnlp.81/",
    pages = "1318--1330",
    ISBN = "979-8-89176-303-6",
    abstract = "Discourse relation parsing plays a crucial role in uncovering the logical structure of text, yet existing corpora focus almost exclusively on general-domain genres, leaving specialized fields like engineering under-resourced. We introduce ENG{-}DRB, the first PDTB{-}style discourse relation corpus derived from transcripts of hands{-}on engineering tutorial videos. ENG{-}DRB comprises 11 tutorials spanning civil, mechanical, and electrical/electronics engineering (155 minutes total) with 1,215 annotated relations. Compared to general{-}domain benchmarks, this dataset features a high proportion of explicit senses, dense causal and temporal relations, and frequent overlapping and embedded senses. Our benchmarking experiments underscore the dataset{'}s difficulty. A top parser (HITS) detects segment boundaries well (98.6{\%} F1), but its relation classification is more than 11 F1 percentages lower than on the standard PDTB. In addition, state{-}of{-}the{-}art LLMs (OpenAI o4{-}mini, Claude 3.7, LLaMA{-}3.1) achieve at best 41{\%} F1 on explicit relations and less than 9{\%} F1 on implicit relations, revealing systematic errors in temporal and causal sense detection. The dataset can be accessed at: https://doi.org/10.57967/hf/6895. Code to reproduce our results is available at: https://github.com/chengzhangedu/ENG-DRB."
}