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            <namePart type="given">Marie-Francine</namePart>
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            <namePart type="given">Xuanjing</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>In the last few years, several methods have been proposed to build meta-embeddings. The general aim was to obtain new representations integrating complementary knowledge from different source pre-trained embeddings thereby improving their overall quality. However, previous meta-embeddings have been evaluated using a variety of methods and datasets, which makes it difficult to draw meaningful conclusions regarding the merits of each approach. In this paper we propose a unified common framework, including both intrinsic and extrinsic tasks, for a fair and objective meta-embeddings evaluation. Furthermore, we present a new method to generate meta-embeddings, outperforming previous work on a large number of intrinsic evaluation benchmarks. Our evaluation framework also allows us to conclude that previous extrinsic evaluations of meta-embeddings have been overestimated.</abstract>
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%0 Conference Proceedings
%T Benchmarking Meta-embeddings: What Works and What Does Not
%A García-Ferrero, Iker
%A Agerri, Rodrigo
%A Rigau, German
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F garcia-ferrero-etal-2021-benchmarking-meta
%X In the last few years, several methods have been proposed to build meta-embeddings. The general aim was to obtain new representations integrating complementary knowledge from different source pre-trained embeddings thereby improving their overall quality. However, previous meta-embeddings have been evaluated using a variety of methods and datasets, which makes it difficult to draw meaningful conclusions regarding the merits of each approach. In this paper we propose a unified common framework, including both intrinsic and extrinsic tasks, for a fair and objective meta-embeddings evaluation. Furthermore, we present a new method to generate meta-embeddings, outperforming previous work on a large number of intrinsic evaluation benchmarks. Our evaluation framework also allows us to conclude that previous extrinsic evaluations of meta-embeddings have been overestimated.
%R 10.18653/v1/2021.findings-emnlp.333
%U https://aclanthology.org/2021.findings-emnlp.333
%U https://doi.org/10.18653/v1/2021.findings-emnlp.333
%P 3957-3972

Markdown (Informal)

[Benchmarking Meta-embeddings: What Works and What Does Not](https://aclanthology.org/2021.findings-emnlp.333) (García-Ferrero et al., Findings 2021)

• Benchmarking Meta-embeddings: What Works and What Does Not (García-Ferrero et al., Findings 2021)

ACL

• Iker García-Ferrero, Rodrigo Agerri, and German Rigau. 2021. Benchmarking Meta-embeddings: What Works and What Does Not. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3957–3972, Punta Cana, Dominican Republic. Association for Computational Linguistics.