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        <title>DagoBERT: Generating Derivational Morphology with a Pretrained Language Model</title>
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    <name type="personal">
        <namePart type="given">Valentin</namePart>
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            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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            <namePart type="family">He</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>Can pretrained language models (PLMs) generate derivationally complex words? We present the first study investigating this question, taking BERT as the example PLM. We examine BERT’s derivational capabilities in different settings, ranging from using the unmodified pretrained model to full finetuning. Our best model, DagoBERT (Derivationally and generatively optimized BERT), clearly outperforms the previous state of the art in derivation generation (DG). Furthermore, our experiments show that the input segmentation crucially impacts BERT’s derivational knowledge, suggesting that the performance of PLMs could be further improved if a morphologically informed vocabulary of units were used.</abstract>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T DagoBERT: Generating Derivational Morphology with a Pretrained Language Model
%A Hofmann, Valentin
%A Pierrehumbert, Janet
%A Schütze, Hinrich
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F hofmann-etal-2020-dagobert
%X Can pretrained language models (PLMs) generate derivationally complex words? We present the first study investigating this question, taking BERT as the example PLM. We examine BERT’s derivational capabilities in different settings, ranging from using the unmodified pretrained model to full finetuning. Our best model, DagoBERT (Derivationally and generatively optimized BERT), clearly outperforms the previous state of the art in derivation generation (DG). Furthermore, our experiments show that the input segmentation crucially impacts BERT’s derivational knowledge, suggesting that the performance of PLMs could be further improved if a morphologically informed vocabulary of units were used.
%R 10.18653/v1/2020.emnlp-main.316
%U https://aclanthology.org/2020.emnlp-main.316
%U https://doi.org/10.18653/v1/2020.emnlp-main.316
%P 3848-3861

Markdown (Informal)

[DagoBERT: Generating Derivational Morphology with a Pretrained Language Model](https://aclanthology.org/2020.emnlp-main.316) (Hofmann et al., EMNLP 2020)

• DagoBERT: Generating Derivational Morphology with a Pretrained Language Model (Hofmann et al., EMNLP 2020)

ACL

• Valentin Hofmann, Janet Pierrehumbert, and Hinrich Schütze. 2020. DagoBERT: Generating Derivational Morphology with a Pretrained Language Model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3848–3861, Online. Association for Computational Linguistics.