Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation

by   Deng Cai, et al.

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 <>.


page 1

page 2

page 3

page 4


Relational Sentence Embedding for Flexible Semantic Matching

We present Relational Sentence Embedding (RSE), a new paradigm to furthe...

Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization

We present Emu, a system that semantically enhances multilingual sentenc...

Semantic Drift in Multilingual Representations

Multilingual representations have mostly been evaluated based on their p...

Sequential Network Transfer: Adapting Sentence Embeddings to Human Activities and Beyond

We study the problem of adapting neural sentence embedding models to the...

Exploring Multilingual Syntactic Sentence Representations

We study methods for learning sentence embeddings with syntactic structu...

EASE: Entity-Aware Contrastive Learning of Sentence Embedding

We present EASE, a novel method for learning sentence embeddings via con...

AMR4NLI: Interpretable and robust NLI measures from semantic graphs

The task of natural language inference (NLI) asks whether a given premis...

Please sign up or login with your details

Forgot password? Click here to reset