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        <title>Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings</title>
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    <name type="personal">
        <namePart type="given">Hanan</namePart>
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    <name type="personal">
        <namePart type="given">Mahesh</namePart>
        <namePart type="family">Mohan</namePart>
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    <name type="personal">
        <namePart type="given">Mona</namePart>
        <namePart type="family">Diab</namePart>
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    <abstract>Most existing methods for automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries. For many language pairs, such supervised alignments are not readily available. We propose an unsupervised approach for learning a bilingual dictionary for a pair of languages given their independently-learned monolingual word embeddings. The proposed method exploits local and global structures in monolingual vector spaces to align them such that similar words are mapped to each other. We show empirically that the performance of bilingual correspondents that are learned using our proposed unsupervised method is comparable to that of using supervised bilingual correspondents from a seed dictionary.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00014</identifier>
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    <part>
        <date>2018</date>
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            <start>185</start>
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%0 Journal Article
%T Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings
%A Aldarmaki, Hanan
%A Mohan, Mahesh
%A Diab, Mona
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F aldarmaki-etal-2018-unsupervised
%X Most existing methods for automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries. For many language pairs, such supervised alignments are not readily available. We propose an unsupervised approach for learning a bilingual dictionary for a pair of languages given their independently-learned monolingual word embeddings. The proposed method exploits local and global structures in monolingual vector spaces to align them such that similar words are mapped to each other. We show empirically that the performance of bilingual correspondents that are learned using our proposed unsupervised method is comparable to that of using supervised bilingual correspondents from a seed dictionary.
%R 10.1162/tacl_a_00014
%U https://aclanthology.org/Q18-1014
%U https://doi.org/10.1162/tacl_a_00014
%P 185-196

Markdown (Informal)

[Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings](https://aclanthology.org/Q18-1014) (Aldarmaki et al., TACL 2018)

• Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings (Aldarmaki et al., TACL 2018)

ACL

• Hanan Aldarmaki, Mahesh Mohan, and Mona Diab. 2018. Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings. Transactions of the Association for Computational Linguistics, 6:185–196.