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        <title>Charmanteau: Character Embedding Models For Portmanteau Creation</title>
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    <name type="personal">
        <namePart type="given">Varun</namePart>
        <namePart type="family">Gangal</namePart>
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    <name type="personal">
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Eduard</namePart>
        <namePart type="family">Hovy</namePart>
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    <name type="personal">
        <namePart type="given">Eric</namePart>
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            <namePart type="given">Rebecca</namePart>
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    <abstract>Portmanteaus are a word formation phenomenon where two words combine into a new word. We propose character-level neural sequence-to-sequence (S2S) methods for the task of portmanteau generation that are end-to-end-trainable, language independent, and do not explicitly use additional phonetic information. We propose a noisy-channel-style model, which allows for the incorporation of unsupervised word lists, improving performance over a standard source-to-target model. This model is made possible by an exhaustive candidate generation strategy specifically enabled by the features of the portmanteau task. Experiments find our approach superior to a state-of-the-art FST-based baseline with respect to ground truth accuracy and human evaluation.</abstract>
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        <date>2017-09</date>
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%0 Conference Proceedings
%T Charmanteau: Character Embedding Models For Portmanteau Creation
%A Gangal, Varun
%A Jhamtani, Harsh
%A Neubig, Graham
%A Hovy, Eduard
%A Nyberg, Eric
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F gangal-etal-2017-charmanteau
%X Portmanteaus are a word formation phenomenon where two words combine into a new word. We propose character-level neural sequence-to-sequence (S2S) methods for the task of portmanteau generation that are end-to-end-trainable, language independent, and do not explicitly use additional phonetic information. We propose a noisy-channel-style model, which allows for the incorporation of unsupervised word lists, improving performance over a standard source-to-target model. This model is made possible by an exhaustive candidate generation strategy specifically enabled by the features of the portmanteau task. Experiments find our approach superior to a state-of-the-art FST-based baseline with respect to ground truth accuracy and human evaluation.
%R 10.18653/v1/D17-1315
%U https://aclanthology.org/D17-1315
%U https://doi.org/10.18653/v1/D17-1315
%P 2917-2922

Markdown (Informal)

[Charmanteau: Character Embedding Models For Portmanteau Creation](https://aclanthology.org/D17-1315) (Gangal et al., EMNLP 2017)

• Charmanteau: Character Embedding Models For Portmanteau Creation (Gangal et al., EMNLP 2017)

ACL

• Varun Gangal, Harsh Jhamtani, Graham Neubig, Eduard Hovy, and Eric Nyberg. 2017. Charmanteau: Character Embedding Models For Portmanteau Creation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2917–2922, Copenhagen, Denmark. Association for Computational Linguistics.