@inproceedings{svoboda-sevcikova-2024-parent,
    title = "{P}a{R}e{NT} (Parent Retrieval Neural Tool): A Deep Dive into Word Formation across Languages",
    author = "Svoboda, Emil  and
      Sevcikova, Magda",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1104",
    pages = "12611--12621",
    abstract = "We present PaReNT (Parent Retrieval Neural Tool), a deep-learning-based multilingual tool performing retrieval and word formation classification in English, German, Dutch, Spanish, French, Russian, and Czech. Parent retrieval refers to determining the lexeme or lexemes the input lexeme was based on (e.g. {``}darkness{''} is traced back to {``}dark{''}; {``}waterfall{''} decomposes into {``}water{''} and {``}fall{''}). Additionally, PaReNT performs word formation classification, which determines the input lexeme as a compound e.g. {``}proofread{''}, a derivative (e.g. {``}deescalate{''}) or as an unmotivated word (e.g. {``}dog{''}). These seven languages are selected from three major branches of the Indo-European language family (Germanic, Romance, Slavic). Data is aggregated from a range of word-formation resources, as well as Wiktionary, to train and test the tool. The tool is based on a custom-architecture hybrid transformer block-enriched sequence-to-sequence neural network utilizing both a character-based and semantic representation of the input lexemes, with two output modules - one decoder-based dedicated to parent retrieval, and one classifier-based for word formation classification. PaReNT achieves a mean accuracy of 0.62 in parent retrieval and a mean balanced accuracy of 0.74 in word formation classification.",
}

We present PaReNT (Parent Retrieval Neural Tool), a deep-learning-based multilingual tool performing retrieval and word formation classification in English, German, Dutch, Spanish, French, Russian, and Czech. Parent retrieval refers to determining the lexeme or lexemes the input lexeme was based on (e.g. {``}darkness{''} is traced back to {``}dark{''}; {``}waterfall{''} decomposes into {``}water{''} and {``}fall{''}). Additionally, PaReNT performs word formation classification, which determines the input lexeme as a compound e.g. {``}proofread{''}, a derivative (e.g. {``}deescalate{''}) or as an unmotivated word (e.g. {``}dog{''}). These seven languages are selected from three major branches of the Indo-European language family (Germanic, Romance, Slavic). Data is aggregated from a range of word-formation resources, as well as Wiktionary, to train and test the tool. The tool is based on a custom-architecture hybrid transformer block-enriched sequence-to-sequence neural network utilizing both a character-based and semantic representation of the input lexemes, with two output modules - one decoder-based dedicated to parent retrieval, and one classifier-based for word formation classification. PaReNT achieves a mean accuracy of 0.62 in parent retrieval and a mean balanced accuracy of 0.74 in word formation classification.
Svoboda, Emil Sevcikova, Magda
PaReNT (Parent Retrieval Neural Tool): A Deep Dive into Word Formation across Languages