@inproceedings{cai-etal-2022-retrofitting,
title = "Retrofitting Multilingual Sentence Embeddings with {A}bstract {M}eaning {R}epresentation",
author = "Cai, Deng and
Li, Xin and
Ho, Jackie Chun-Sing and
Bing, Lidong and
Lam, Wai",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.433",
doi = "10.18653/v1/2022.emnlp-main.433",
pages = "6456--6472",
abstract = "We introduce a new method to improve existing multilingual sentence embeddings with Abstract Meaning Representation (AMR). Compared with the original textual input, AMR is a structured semantic representation that presents the core concepts and relations in a sentence explicitly and unambiguously. It also helps reduce the surface variations across different expressions and languages. Unlike most prior work that only evaluates the ability to measure semantic similarity, we present a thorough evaluation of existing multilingual sentence embeddings and our improved versions, which include a collection of five transfer tasks in different downstream applications. Experiment results show that retrofitting multilingual sentence embeddings with AMR leads to better state-of-the-art performance on both semantic textual similarity and transfer tasks.",
}
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<abstract>We introduce a new method to improve existing multilingual sentence embeddings with Abstract Meaning Representation (AMR). Compared with the original textual input, AMR is a structured semantic representation that presents the core concepts and relations in a sentence explicitly and unambiguously. It also helps reduce the surface variations across different expressions and languages. Unlike most prior work that only evaluates the ability to measure semantic similarity, we present a thorough evaluation of existing multilingual sentence embeddings and our improved versions, which include a collection of five transfer tasks in different downstream applications. Experiment results show that retrofitting multilingual sentence embeddings with AMR leads to better state-of-the-art performance on both semantic textual similarity and transfer tasks.</abstract>
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%0 Conference Proceedings
%T Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation
%A Cai, Deng
%A Li, Xin
%A Ho, Jackie Chun-Sing
%A Bing, Lidong
%A Lam, Wai
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F cai-etal-2022-retrofitting
%X We introduce a new method to improve existing multilingual sentence embeddings with Abstract Meaning Representation (AMR). Compared with the original textual input, AMR is a structured semantic representation that presents the core concepts and relations in a sentence explicitly and unambiguously. It also helps reduce the surface variations across different expressions and languages. Unlike most prior work that only evaluates the ability to measure semantic similarity, we present a thorough evaluation of existing multilingual sentence embeddings and our improved versions, which include a collection of five transfer tasks in different downstream applications. Experiment results show that retrofitting multilingual sentence embeddings with AMR leads to better state-of-the-art performance on both semantic textual similarity and transfer tasks.
%R 10.18653/v1/2022.emnlp-main.433
%U https://aclanthology.org/2022.emnlp-main.433
%U https://doi.org/10.18653/v1/2022.emnlp-main.433
%P 6456-6472
Markdown (Informal)
[Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation](https://aclanthology.org/2022.emnlp-main.433) (Cai et al., EMNLP 2022)
ACL