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        <title>Transformed Protoform Reconstruction</title>
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    <name type="personal">
        <namePart type="given">Young</namePart>
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            <title>Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
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            <namePart type="given">Naoaki</namePart>
            <namePart type="family">Okazaki</namePart>
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    <abstract>Protoform reconstruction is the task of inferring what morphemes or words appeared like in the ancestral languages of a set of daughter languages. Meloni et al (2021) achieved the state-of-the-art on Latin protoform reconstruction with an RNN-based encoder-decoder with attention model. We update their model with the state-of-the-art seq2seq model: the Transformer. Our model outperforms their model on a suite of different metrics on two different datasets: their Romance data of 8,000 cognates spanning 5 languages and a Chinese dataset (Hou 2004) of 800+ cognates spanning 39 varieties. We also probe our model for potential phylogenetic signal contained in the model. Our code is publicly available at https://github.com/cmu-llab/acl-2023.</abstract>
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    <identifier type="doi">10.18653/v1/2023.acl-short.3</identifier>
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        <url>https://aclanthology.org/2023.acl-short.3</url>
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        <date>2023-07</date>
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            <start>24</start>
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%0 Conference Proceedings
%T Transformed Protoform Reconstruction
%A Kim, Young Min
%A Chang, Kalvin
%A Cui, Chenxuan
%A Mortensen, David R.
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kim-etal-2023-transformed
%X Protoform reconstruction is the task of inferring what morphemes or words appeared like in the ancestral languages of a set of daughter languages. Meloni et al (2021) achieved the state-of-the-art on Latin protoform reconstruction with an RNN-based encoder-decoder with attention model. We update their model with the state-of-the-art seq2seq model: the Transformer. Our model outperforms their model on a suite of different metrics on two different datasets: their Romance data of 8,000 cognates spanning 5 languages and a Chinese dataset (Hou 2004) of 800+ cognates spanning 39 varieties. We also probe our model for potential phylogenetic signal contained in the model. Our code is publicly available at https://github.com/cmu-llab/acl-2023.
%R 10.18653/v1/2023.acl-short.3
%U https://aclanthology.org/2023.acl-short.3
%U https://doi.org/10.18653/v1/2023.acl-short.3
%P 24-38

Markdown (Informal)

[Transformed Protoform Reconstruction](https://aclanthology.org/2023.acl-short.3) (Kim et al., ACL 2023)

• Transformed Protoform Reconstruction (Kim et al., ACL 2023)

ACL

• Young Min Kim, Kalvin Chang, Chenxuan Cui, and David R. Mortensen. 2023. Transformed Protoform Reconstruction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 24–38, Toronto, Canada. Association for Computational Linguistics.