@inproceedings{bruckner-etal-2026-oral,
    title = "From Oral History to Structured Data: The {M}alach{NER} Dataset",
    author = {Br{\"u}ckner, Christopher  and
      Roginer Hofmeister, Karin  and
      Koci{\'a}n, Ji{\v{r}}{\'i}  and
      Pecina, Pavel},
    editor = "Anuradha, Isuri  and
      Wynne, Martin",
    booktitle = "Proceedings of The Second Workshop on Holocaust Testimonies as Language Resources ({HTR}es)",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.htres-2.7/",
    doi = "10.63317/3zyb8y48bdnk",
    pages = "59--65",
    abstract = "We present MalachNER, a new multilingual dataset for Named Entity Recognition (NER) in testimonies of Holocaust survivors. MalachNER has been sourced from different archives and annotated based on comprehensive domain-specific guidelines refined by a collaboration of international experts. Covering 10 European languages, differs significantly from previously released datasets: It is primarily based on noisy, verbatim transcribed speech, rather than on digitized written documents. These transcripts are characterized, among other challenges, by fillers, dialectal speech, and in-line annotations indicating incomprehensible words, which are not commonly encountered in other datasets. However, large volumes of yet unprocessed oral history make such a dataset a necessity. In addition to the description of the dataset and its annotation guidelines, we show with baseline experiments that MalachNER is complementary with previously released data, and the key to training domain-specific language models that generalize well to written and oral testimony alike, achieving state-of-the-art performance on both types of documents."
}