@inproceedings{vylomova-etal-2020-sigmorphon,
    title = "{SIGMORPHON} 2020 Shared Task 0: Typologically Diverse Morphological Inflection",
    author = "Vylomova, Ekaterina  and
      White, Jennifer  and
      Salesky, Elizabeth  and
      Mielke, Sabrina J.  and
      Wu, Shijie  and
      Ponti, Edoardo Maria  and
      Maudslay, Rowan Hall  and
      Zmigrod, Ran  and
      Valvoda, Josef  and
      Toldova, Svetlana  and
      Tyers, Francis  and
      Klyachko, Elena  and
      Yegorov, Ilya  and
      Krizhanovsky, Natalia  and
      Czarnowska, Paula  and
      Nikkarinen, Irene  and
      Krizhanovsky, Andrew  and
      Pimentel, Tiago  and
      Torroba Hennigen, Lucas  and
      Kirov, Christo  and
      Nicolai, Garrett  and
      Williams, Adina  and
      Anastasopoulos, Antonios  and
      Cruz, Hilaria  and
      Chodroff, Eleanor  and
      Cotterell, Ryan  and
      Silfverberg, Miikka  and
      Hulden, Mans",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.sigmorphon-1.1",
    doi = "10.18653/v1/2020.sigmorphon-1.1",
    pages = "1--39",
    abstract = "A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from just one language such as English. The SIGMORPHON 2020 shared task on morphological reinflection aims to investigate systems{'} ability to generalize across typologically distinct languages, many of which are low resource. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages. A total of 22 systems (19 neural) from 10 teams were submitted to the task. All four winning systems were neural (two monolingual transformers and two massively multilingual RNN-based models with gated attention). Most teams demonstrate utility of data hallucination and augmentation, ensembles, and multilingual training for low-resource languages. Non-neural learners and manually designed grammars showed competitive and even superior performance on some languages (such as Ingrian, Tajik, Tagalog, Zarma, Lingala), especially with very limited data. Some language families (Afro-Asiatic, Niger-Congo, Turkic) were relatively easy for most systems and achieved over 90{\%} mean accuracy while others were more challenging.",
}