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        <title>Addressing Low-Resource Scenarios with Character-aware Embeddings</title>
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        <namePart type="given">Sean</namePart>
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            <title>Proceedings of the Second Workshop on Subword/Character LEvel Models</title>
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            <namePart type="given">Manaal</namePart>
            <namePart type="family">Faruqui</namePart>
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            <namePart type="given">Yadollah</namePart>
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    <abstract>Most modern approaches to computing word embeddings assume the availability of text corpora with billions of words. In this paper, we explore a setup where only corpora with millions of words are available, and many words in any new text are out of vocabulary. This setup is both of practical interests – modeling the situation for specific domains and low-resource languages – and of psycholinguistic interest, since it corresponds much more closely to the actual experiences and challenges of human language learning and use. We compare standard skip-gram word embeddings with character-based embeddings on word relatedness prediction. Skip-grams excel on large corpora, while character-based embeddings do well on small corpora generally and rare and complex words specifically. The models can be combined easily.</abstract>
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%0 Conference Proceedings
%T Addressing Low-Resource Scenarios with Character-aware Embeddings
%A Papay, Sean
%A Padó, Sebastian
%A Vu, Ngoc Thang
%Y Faruqui, Manaal
%Y Schütze, Hinrich
%Y Trancoso, Isabel
%Y Tsvetkov, Yulia
%Y Yaghoobzadeh, Yadollah
%S Proceedings of the Second Workshop on Subword/Character LEvel Models
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans
%F papay-etal-2018-addressing
%X Most modern approaches to computing word embeddings assume the availability of text corpora with billions of words. In this paper, we explore a setup where only corpora with millions of words are available, and many words in any new text are out of vocabulary. This setup is both of practical interests – modeling the situation for specific domains and low-resource languages – and of psycholinguistic interest, since it corresponds much more closely to the actual experiences and challenges of human language learning and use. We compare standard skip-gram word embeddings with character-based embeddings on word relatedness prediction. Skip-grams excel on large corpora, while character-based embeddings do well on small corpora generally and rare and complex words specifically. The models can be combined easily.
%R 10.18653/v1/W18-1204
%U https://aclanthology.org/W18-1204
%U https://doi.org/10.18653/v1/W18-1204
%P 32-37

Markdown (Informal)

[Addressing Low-Resource Scenarios with Character-aware Embeddings](https://aclanthology.org/W18-1204) (Papay et al., SCLeM 2018)

• Addressing Low-Resource Scenarios with Character-aware Embeddings (Papay et al., SCLeM 2018)

ACL

• Sean Papay, Sebastian Padó, and Ngoc Thang Vu. 2018. Addressing Low-Resource Scenarios with Character-aware Embeddings. In Proceedings of the Second Workshop on Subword/Character LEvel Models, pages 32–37, New Orleans. Association for Computational Linguistics.