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metadata
dataset_info:
  features:
    - name: index
      dtype: uint32
    - name: audio
      dtype: audio
    - name: text_en
      dtype: string
    - name: text_ar
      dtype: string
    - name: text_de
      dtype: string
    - name: text_fa
      dtype: string
    - name: text_fr
      dtype: string
    - name: text_ja
      dtype: string
    - name: text_nl
      dtype: string
    - name: text_pt
      dtype: string
    - name: text_ru
      dtype: string
    - name: text_tr
      dtype: string
    - name: text_zh
      dtype: string
  splits:
    - name: dev
      num_bytes: 100170127
      num_examples: 468
    - name: eval
      num_bytes: 98822395
      num_examples: 416
  download_size: 196166661
  dataset_size: 198992522
configs:
  - config_name: default
    data_files:
      - split: dev
        path: data/dev-*
      - split: eval
        path: data/eval-*
license: cc-by-4.0
language:
  - en
  - ar
  - de
  - fa
  - fr
  - ja
  - nl
  - pt
  - ru
  - tr
  - zh
task_categories:
  - translation
  - automatic-speech-recognition
size_categories:
  - n<1K

ACL 60/60

Dataset details

ACL 60/60 evaluation sets for multilingual translation of ACL 2022 technical presentations into 10 target languages.

Citation

@inproceedings{salesky-etal-2023-evaluating,
    title = "Evaluating Multilingual Speech Translation under Realistic Conditions with Resegmentation and Terminology",
    author = "Salesky, Elizabeth  and
      Darwish, Kareem  and
      Al-Badrashiny, Mohamed  and
      Diab, Mona  and
      Niehues, Jan",
    booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.iwslt-1.2/",
    doi = "10.18653/v1/2023.iwslt-1.2",
    pages = "62--78",
    abstract = "We present the ACL 60/60 evaluation sets for multilingual translation of ACL 2022 technical presentations into 10 target languages. This dataset enables further research into multilingual speech translation under realistic recording conditions with unsegmented audio and domain-specific terminology, applying NLP tools to text and speech in the technical domain, and evaluating and improving model robustness to diverse speaker demographics."
}