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metadata
language:
  - en
license: apache-2.0
dataset_info:
  config_name: zho.dep.scidtb.rels
  features:
    - name: doc
      dtype: string
    - name: unit1_toks
      dtype: string
    - name: unit2_toks
      dtype: string
    - name: unit1_txt
      dtype: string
    - name: unit2_txt
      dtype: string
    - name: s1_toks
      dtype: string
    - name: s2_toks
      dtype: string
    - name: unit1_sent
      dtype: string
    - name: unit2_sent
      dtype: string
    - name: dir
      dtype: string
    - name: orig_label
      dtype: string
    - name: label
      dtype: string
  splits:
    - name: train
      num_bytes: 628861
      num_examples: 802
    - name: validation
      num_bytes: 228839
      num_examples: 281
    - name: test
      num_bytes: 181790
      num_examples: 215
  download_size: 254512
  dataset_size: 1039490
configs:
  - config_name: zho.dep.scidtb.rels
    data_files:
      - split: train
        path: zho.dep.scidtb.rels/train-*
      - split: validation
        path: zho.dep.scidtb.rels/validation-*
      - split: test
        path: zho.dep.scidtb.rels/test-*

https://github.com/disrpt/sharedtask2023

scditb:

@inproceedings{yang-li-2018-scidtb,
    title = "{S}ci{DTB}: Discourse Dependency {T}ree{B}ank for Scientific Abstracts",
    author = "Yang, An  and
      Li, Sujian",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-2071",
    doi = "10.18653/v1/P18-2071",
    pages = "444--449",
    abstract = "Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question answering. In this paper, we present SciDTB, a domain-specific discourse treebank annotated on scientific articles. Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is flexible and simplified to some extent but do not sacrifice structural integrity. We discuss the labeling framework, annotation workflow and some statistics about SciDTB. Furthermore, our treebank is made as a benchmark for evaluating discourse dependency parsers, on which we provide several baselines as fundamental work.",
}