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        <title>Cross-lingual Inflection as a Data Augmentation Method for Parsing</title>
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        <namePart type="given">Alberto</namePart>
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        <namePart type="given">Carlos</namePart>
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            <title>Proceedings of the Third Workshop on Insights from Negative Results in NLP</title>
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        <name type="personal">
            <namePart type="given">Shabnam</namePart>
            <namePart type="family">Tafreshi</namePart>
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            <namePart type="given">João</namePart>
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            <namePart type="given">Anna</namePart>
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            <namePart type="given">Arjun</namePart>
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    <abstract>We propose a morphology-based method for low-resource (LR) dependency parsing. We train a morphological inflector for target LR languages, and apply it to related rich-resource (RR) treebanks to create cross-lingual (x-inflected) treebanks that resemble the target LR language. We use such inflected treebanks to train parsers in zero- (training on x-inflected treebanks) and few-shot (training on x-inflected and target language treebanks) setups. The results show that the method sometimes improves the baselines, but not consistently.</abstract>
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    <identifier type="doi">10.18653/v1/2022.insights-1.7</identifier>
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%0 Conference Proceedings
%T Cross-lingual Inflection as a Data Augmentation Method for Parsing
%A Muñoz-Ortiz, Alberto
%A Gómez-Rodríguez, Carlos
%A Vilares, David
%Y Tafreshi, Shabnam
%Y Sedoc, João
%Y Rogers, Anna
%Y Drozd, Aleksandr
%Y Rumshisky, Anna
%Y Akula, Arjun
%S Proceedings of the Third Workshop on Insights from Negative Results in NLP
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F munoz-ortiz-etal-2022-cross
%X We propose a morphology-based method for low-resource (LR) dependency parsing. We train a morphological inflector for target LR languages, and apply it to related rich-resource (RR) treebanks to create cross-lingual (x-inflected) treebanks that resemble the target LR language. We use such inflected treebanks to train parsers in zero- (training on x-inflected treebanks) and few-shot (training on x-inflected and target language treebanks) setups. The results show that the method sometimes improves the baselines, but not consistently.
%R 10.18653/v1/2022.insights-1.7
%U https://aclanthology.org/2022.insights-1.7
%U https://doi.org/10.18653/v1/2022.insights-1.7
%P 54-61

Markdown (Informal)

[Cross-lingual Inflection as a Data Augmentation Method for Parsing](https://aclanthology.org/2022.insights-1.7) (Muñoz-Ortiz et al., insights 2022)

• Cross-lingual Inflection as a Data Augmentation Method for Parsing (Muñoz-Ortiz et al., insights 2022)

ACL

• Alberto Muñoz-Ortiz, Carlos Gómez-Rodríguez, and David Vilares. 2022. Cross-lingual Inflection as a Data Augmentation Method for Parsing. In Proceedings of the Third Workshop on Insights from Negative Results in NLP, pages 54–61, Dublin, Ireland. Association for Computational Linguistics.