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- W4320234827 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 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. Our codebase and evaluation scripts can be found at url{https://github.com/jcyk/MSE-AMR}." @default.
- W4320234827 created "2023-02-13" @default.
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- W4320234827 date "2022-10-18" @default.
- W4320234827 modified "2023-10-18" @default.
- W4320234827 title "Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation" @default.
- W4320234827 doi "https://doi.org/10.48550/arxiv.2210.09773" @default.
- W4320234827 hasPublicationYear "2022" @default.
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