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        <title>Convolutional Neural Network for Universal Sentence Embeddings</title>
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        <namePart type="given">Xiaoqi</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
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    <abstract>This paper proposes a simple CNN model for creating general-purpose sentence embeddings that can transfer easily across domains and can also act as effective initialization for downstream tasks. Recently, averaging the embeddings of words in a sentence has proven to be a surprisingly successful and efficient way of obtaining sentence embeddings. However, these models represent a sentence, only in terms of features of words or uni-grams in it. In contrast, our model (CSE) utilizes both features of words and n-grams to encode sentences, which is actually a generalization of these bag-of-words models. The extensive experiments demonstrate that CSE performs better than average models in transfer learning setting and exceeds the state of the art in supervised learning setting by initializing the parameters with the pre-trained sentence embeddings.</abstract>
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%0 Conference Proceedings
%T Convolutional Neural Network for Universal Sentence Embeddings
%A Jiao, Xiaoqi
%A Wang, Fang
%A Feng, Dan
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F jiao-etal-2018-convolutional
%X This paper proposes a simple CNN model for creating general-purpose sentence embeddings that can transfer easily across domains and can also act as effective initialization for downstream tasks. Recently, averaging the embeddings of words in a sentence has proven to be a surprisingly successful and efficient way of obtaining sentence embeddings. However, these models represent a sentence, only in terms of features of words or uni-grams in it. In contrast, our model (CSE) utilizes both features of words and n-grams to encode sentences, which is actually a generalization of these bag-of-words models. The extensive experiments demonstrate that CSE performs better than average models in transfer learning setting and exceeds the state of the art in supervised learning setting by initializing the parameters with the pre-trained sentence embeddings.
%U https://aclanthology.org/C18-1209
%P 2470-2481

Markdown (Informal)

[Convolutional Neural Network for Universal Sentence Embeddings](https://aclanthology.org/C18-1209) (Jiao et al., COLING 2018)

• Convolutional Neural Network for Universal Sentence Embeddings (Jiao et al., COLING 2018)

ACL

• Xiaoqi Jiao, Fang Wang, and Dan Feng. 2018. Convolutional Neural Network for Universal Sentence Embeddings. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2470–2481, Santa Fe, New Mexico, USA. Association for Computational Linguistics.