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    <abstract>Word embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) models. The popular continuous bag-of-words (CBOW) model of word2vec learns a vector embedding by masking a given word in a sentence and then using the other words as a context to predict it. A limitation of CBOW is that it equally weights the context words when making a prediction, which is inefficient, since some words have higher predictive value than others. We tackle this inefficiency by introducing the Attention Word Embedding (AWE) model, which integrates the attention mechanism into the CBOW model. We also propose AWE-S, which incorporates subword information. We demonstrate that AWE and AWE-S outperform the state-of-the-art word embedding models both on a variety of word similarity datasets and when used for initialization of NLP models.</abstract>
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%0 Conference Proceedings
%T Attention Word Embedding
%A Sonkar, Shashank
%A Waters, Andrew
%A Baraniuk, Richard
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F sonkar-etal-2020-attention
%X Word embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) models. The popular continuous bag-of-words (CBOW) model of word2vec learns a vector embedding by masking a given word in a sentence and then using the other words as a context to predict it. A limitation of CBOW is that it equally weights the context words when making a prediction, which is inefficient, since some words have higher predictive value than others. We tackle this inefficiency by introducing the Attention Word Embedding (AWE) model, which integrates the attention mechanism into the CBOW model. We also propose AWE-S, which incorporates subword information. We demonstrate that AWE and AWE-S outperform the state-of-the-art word embedding models both on a variety of word similarity datasets and when used for initialization of NLP models.
%R 10.18653/v1/2020.coling-main.608
%U https://aclanthology.org/2020.coling-main.608
%U https://doi.org/10.18653/v1/2020.coling-main.608
%P 6894-6902

Markdown (Informal)

[Attention Word Embedding](https://aclanthology.org/2020.coling-main.608) (Sonkar et al., COLING 2020)

• Attention Word Embedding (Sonkar et al., COLING 2020)

ACL

• Shashank Sonkar, Andrew Waters, and Richard Baraniuk. 2020. Attention Word Embedding. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6894–6902, Barcelona, Spain (Online). International Committee on Computational Linguistics.