Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation
Deng Cai, Xin Li, Jackie Chun-Sing Ho, Lidong Bing, Wai Pang Lam · 2022
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