---
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."
}

```