Graph-Assisted Culturally Adaptable Idiomatic Translation for Indic Languages

TL;DR

This paper introduces IdiomCE, a graph neural network approach that improves idiomatic translation for Indian languages by capturing complex cultural and contextual nuances, outperforming traditional methods especially in resource-limited scenarios.

Contribution

The paper presents a novel GNN-based method, IdiomCE, for translating idioms across languages, effectively handling unseen expressions and cultural variations.

Findings

Significant improvement in translation quality over baseline methods

Effective generalization to unseen idioms and expressions

Enhanced performance in resource-constrained settings

Abstract

Translating multi-word expressions (MWEs) and idioms requires a deep understanding of the cultural nuances of both the source and target languages. This challenge is further amplified by the one-to-many nature of idiomatic translations, where a single source idiom can have multiple target-language equivalents depending on cultural references and contextual variations. Traditional static knowledge graphs (KGs) and prompt-based approaches struggle to capture these complex relationships, often leading to suboptimal translations. To address this, we propose IdiomCE, an adaptive graph neural network (GNN) based methodology that learns intricate mappings between idiomatic expressions, effectively generalizing to both seen and unseen nodes during training. Our proposed method enhances translation quality even in resource-constrained settings, facilitating improved idiomatic translation in smaller models. We evaluate our approach on multiple idiomatic translation datasets using reference-less metrics, demonstrating significant improvements in translating idioms from English to various Indian languages.