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    <abstract>Creating accurate meta-embeddings from pre-trained source embeddings has received attention lately. Methods based on global and locally-linear transformation and concatenation have shown to produce accurate meta-embeddings. In this paper, we show that the arithmetic mean of two distinct word embedding sets yields a performant meta-embedding that is comparable or better than more complex meta-embedding learning methods. The result seems counter-intuitive given that vector spaces in different source embeddings are not comparable and cannot be simply averaged. We give insight into why averaging can still produce accurate meta-embedding despite the incomparability of the source vector spaces.</abstract>
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%0 Conference Proceedings
%T Frustratingly Easy Meta-Embedding – Computing Meta-Embeddings by Averaging Source Word Embeddings
%A Coates, Joshua
%A Bollegala, Danushka
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F coates-bollegala-2018-frustratingly
%X Creating accurate meta-embeddings from pre-trained source embeddings has received attention lately. Methods based on global and locally-linear transformation and concatenation have shown to produce accurate meta-embeddings. In this paper, we show that the arithmetic mean of two distinct word embedding sets yields a performant meta-embedding that is comparable or better than more complex meta-embedding learning methods. The result seems counter-intuitive given that vector spaces in different source embeddings are not comparable and cannot be simply averaged. We give insight into why averaging can still produce accurate meta-embedding despite the incomparability of the source vector spaces.
%R 10.18653/v1/N18-2031
%U https://aclanthology.org/N18-2031
%U https://doi.org/10.18653/v1/N18-2031
%P 194-198

Markdown (Informal)

[Frustratingly Easy Meta-Embedding – Computing Meta-Embeddings by Averaging Source Word Embeddings](https://aclanthology.org/N18-2031) (Coates & Bollegala, NAACL 2018)

• Frustratingly Easy Meta-Embedding – Computing Meta-Embeddings by Averaging Source Word Embeddings (Coates & Bollegala, NAACL 2018)

ACL

• Joshua Coates and Danushka Bollegala. 2018. Frustratingly Easy Meta-Embedding – Computing Meta-Embeddings by Averaging Source Word Embeddings. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 194–198, New Orleans, Louisiana. Association for Computational Linguistics.