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        <title>Exploring Multilingual Syntactic Sentence Representations</title>
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    <name type="personal">
        <namePart type="given">Chen</namePart>
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        <namePart type="family">De Andrade</namePart>
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    <name type="personal">
        <namePart type="given">Muhammad</namePart>
        <namePart type="family">Osama</namePart>
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        <dateIssued>2019-nov</dateIssued>
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            <title>Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)</title>
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    <abstract>We study methods for learning sentence embeddings with syntactic structure. We focus on methods of learning syntactic sentence-embeddings by using a multilingual parallel-corpus augmented by Universal Parts-of-Speech tags. We evaluate the quality of the learned embeddings by examining sentence-level nearest neighbours and functional dissimilarity in the embedding space. We also evaluate the ability of the method to learn syntactic sentence-embeddings for low-resource languages and demonstrate strong evidence for transfer learning. Our results show that syntactic sentence-embeddings can be learned while using less training data, fewer model parameters, and resulting in better evaluation metrics than state-of-the-art language models.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5521</identifier>
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        <date>2019-nov</date>
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            <start>153</start>
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%0 Conference Proceedings
%T Exploring Multilingual Syntactic Sentence Representations
%A Liu, Chen
%A De Andrade, Anderson
%A Osama, Muhammad
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 nov
%I Association for Computational Linguistics
%C Hong Kong, China
%F liu-etal-2019-exploring
%X We study methods for learning sentence embeddings with syntactic structure. We focus on methods of learning syntactic sentence-embeddings by using a multilingual parallel-corpus augmented by Universal Parts-of-Speech tags. We evaluate the quality of the learned embeddings by examining sentence-level nearest neighbours and functional dissimilarity in the embedding space. We also evaluate the ability of the method to learn syntactic sentence-embeddings for low-resource languages and demonstrate strong evidence for transfer learning. Our results show that syntactic sentence-embeddings can be learned while using less training data, fewer model parameters, and resulting in better evaluation metrics than state-of-the-art language models.
%R 10.18653/v1/D19-5521
%U https://aclanthology.org/D19-5521
%U https://doi.org/10.18653/v1/D19-5521
%P 153-159

Markdown (Informal)

[Exploring Multilingual Syntactic Sentence Representations](https://aclanthology.org/D19-5521) (Liu et al., EMNLP 2019)

• Exploring Multilingual Syntactic Sentence Representations (Liu et al., EMNLP 2019)

ACL

• Chen Liu, Anderson De Andrade, and Muhammad Osama. 2019. Exploring Multilingual Syntactic Sentence Representations. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 153–159, Hong Kong, China. Association for Computational Linguistics.