SEACrowd/qed

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The checks could not identify a recognised licence ID in the dataset's metadata.

Declared licence: other, from the tagEvidence: README.md at edfe95d
Licence files: LICENSE, licence not identifiedEvidence: LICENSE at edfe95d
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Citation text from the card

@inproceedings{abdelali-etal-2014-amara,
    title = "The {AMARA} Corpus: Building Parallel Language Resources for the Educational Domain",
    author = "Abdelali, Ahmed  and
      Guzman, Francisco  and
      Sajjad, Hassan  and
      Vogel, Stephan",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Loftsson, Hrafn  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
    month = may,
    year = "2014",
    address = "Reykjavik, Iceland",
    publisher = "European Language Resources Association (ELRA)",
    url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/877_Paper.pdf",
    pages = "1856--1862",
    abstract = "This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190 parallel corpora.
    This corpus includes both resource-rich languages such as English and Arabic, and resource-poor languages such as Hindi and Thai. In this paper, we describe the gathering, validation, and preprocessing of a large collection of parallel,
    community-generated subtitles. Furthermore, we describe the methodology used to prepare the data for Machine Translation tasks. Additionally, we provide a

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dataset_licence({ "id": "SEACrowd/qed" })
signal: "unclear"
stated_licence: "other"
sources: 0