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        <title>Weird Inflects but OK: Making Sense of Morphological Generation Errors</title>
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    <name type="personal">
        <namePart type="given">Kyle</namePart>
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        <namePart type="given">Arya</namePart>
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    <name type="personal">
        <namePart type="given">Ekaterina</namePart>
        <namePart type="family">Vylomova</namePart>
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    <name type="personal">
        <namePart type="given">Miikka</namePart>
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    <name type="personal">
        <namePart type="given">Magdalena</namePart>
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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    <abstract>We conduct a manual error analysis of the CoNLL-SIGMORPHON Shared Task on Morphological Reinflection. This task involves natural language generation: systems are given a word in citation form (e.g., hug) and asked to produce the corresponding inflected form (e.g., the simple past hugged). We propose an error taxonomy and use it to annotate errors made by the top two systems across twelve languages. Many of the observed errors are related to inflectional patterns sensitive to inherent linguistic properties such as animacy or affect; many others are failures to predict truly unpredictable inflectional behaviors. We also find nearly one quarter of the residual “errors” reflect errors in the gold data.</abstract>
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%0 Conference Proceedings
%T Weird Inflects but OK: Making Sense of Morphological Generation Errors
%A Gorman, Kyle
%A McCarthy, Arya D.
%A Cotterell, Ryan
%A Vylomova, Ekaterina
%A Silfverberg, Miikka
%A Markowska, Magdalena
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F gorman-etal-2019-weird
%X We conduct a manual error analysis of the CoNLL-SIGMORPHON Shared Task on Morphological Reinflection. This task involves natural language generation: systems are given a word in citation form (e.g., hug) and asked to produce the corresponding inflected form (e.g., the simple past hugged). We propose an error taxonomy and use it to annotate errors made by the top two systems across twelve languages. Many of the observed errors are related to inflectional patterns sensitive to inherent linguistic properties such as animacy or affect; many others are failures to predict truly unpredictable inflectional behaviors. We also find nearly one quarter of the residual “errors” reflect errors in the gold data.
%R 10.18653/v1/K19-1014
%U https://aclanthology.org/K19-1014
%U https://doi.org/10.18653/v1/K19-1014
%P 140-151

Markdown (Informal)

[Weird Inflects but OK: Making Sense of Morphological Generation Errors](https://aclanthology.org/K19-1014) (Gorman et al., CoNLL 2019)

• Weird Inflects but OK: Making Sense of Morphological Generation Errors (Gorman et al., CoNLL 2019)

ACL

• Kyle Gorman, Arya D. McCarthy, Ryan Cotterell, Ekaterina Vylomova, Miikka Silfverberg, and Magdalena Markowska. 2019. Weird Inflects but OK: Making Sense of Morphological Generation Errors. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 140–151, Hong Kong, China. Association for Computational Linguistics.