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        <title>Meta-Embedding Sentence Representation for Textual Similarity</title>
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        <namePart type="given">Amir</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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    <abstract>Word embedding models are now widely used in most NLP applications. Despite their effectiveness, there is no clear evidence about the choice of the most appropriate model. It often depends on the nature of the task and on the quality and size of the used data sets. This remains true for bottom-up sentence embedding models. However, no straightforward investigation has been conducted so far. In this paper, we propose a systematic study of the impact of the main word embedding models on sentence representation. By contrasting in-domain and pre-trained embedding models, we show under which conditions they can be jointly used for bottom-up sentence embeddings. Finally, we propose the first bottom-up meta-embedding representation at the sentence level for textual similarity. Significant improvements are observed in several tasks including question-to-question similarity, paraphrasing and next utterance ranking.</abstract>
    <identifier type="citekey">hazem-hernandez-2019-meta</identifier>
    <identifier type="doi">10.26615/978-954-452-056-4_055</identifier>
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        <url>https://aclanthology.org/R19-1055</url>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T Meta-Embedding Sentence Representation for Textual Similarity
%A Hazem, Amir
%A Hernandez, Nicolas
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F hazem-hernandez-2019-meta
%X Word embedding models are now widely used in most NLP applications. Despite their effectiveness, there is no clear evidence about the choice of the most appropriate model. It often depends on the nature of the task and on the quality and size of the used data sets. This remains true for bottom-up sentence embedding models. However, no straightforward investigation has been conducted so far. In this paper, we propose a systematic study of the impact of the main word embedding models on sentence representation. By contrasting in-domain and pre-trained embedding models, we show under which conditions they can be jointly used for bottom-up sentence embeddings. Finally, we propose the first bottom-up meta-embedding representation at the sentence level for textual similarity. Significant improvements are observed in several tasks including question-to-question similarity, paraphrasing and next utterance ranking.
%R 10.26615/978-954-452-056-4_055
%U https://aclanthology.org/R19-1055
%U https://doi.org/10.26615/978-954-452-056-4_055
%P 465-473

Markdown (Informal)

[Meta-Embedding Sentence Representation for Textual Similarity](https://aclanthology.org/R19-1055) (Hazem & Hernandez, RANLP 2019)

• Meta-Embedding Sentence Representation for Textual Similarity (Hazem & Hernandez, RANLP 2019)

ACL

• Amir Hazem and Nicolas Hernandez. 2019. Meta-Embedding Sentence Representation for Textual Similarity. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 465–473, Varna, Bulgaria. INCOMA Ltd..