Graph-Assisted Culturally Adaptable Idiomatic Translation for I ndic languages (2025)

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 an 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

## introduction in linguistic terms, idiom is a multi-word expression (mwe) whose meaning cannot be derived from the literal meanings of its individual parts. idioms have key properties such as noncompositionality, fixedness, and cultural specificity @xcite . they are integral to everyday language, enhancing expressiveness and communicative vividness. they often originate from diverse cultural, historical, and situational contexts, making them unique to specific languages or regions @xcite . with advancements in large language models (llms), neural machine translation (nmt) has significantly improved in handling complex linguistic phenomena, which led to research interest in every cloud has a silver lining idiom concept behind every difficulty or period of gloom, there is the promise of hope values reflects a deep-rooted cultural optimism situational context used to console or inspire someone facing challenges phrase draws on centuries to symbolize hope emerging from darkness historical context idiomce h5 h6 h7 h8 e2 e1 h1 h2 h3 h4 recent efforts to address idiomatic translation have primarily relied on (1) idiom dictionary-based substitution @xcite and (2) prompting techniques, such as chain-of-thought (cot) reasoning or explicitly providing figurative meanings in prompts @xcite @xcite . although these methods have shown improvements in idiomatic translation, they still fail to overcome key challenges. as shown in figure 1 , these methods often overlook cultural factors that shape idioms and influence their mappings across languages (challenge i). additionally, they fail to address the oneto-many nature of idioms, where a single sourcelanguage idiom may have multiple valid translations in the target language, with the optimal choice depending on the source sentence's context @xcite (challenge ii). moreover, knowledge graph (kg)-based approaches are inherently constrained by the availability of idiom resources, leading to translation gaps when encountering idioms not present in the kg @xcite (challenge iii). these challenges pose a critical research question: how can cultural nuances be effectively integrated into many-to-many idiomatic translation to enhance model performance? to address this challenge, one possible approach is to first analyze the cultural dependencies of idioms and identify the specific cultural elements that shape idiomatic expressions across languages. recent studies in nlp @xcite introduce a comprehensive taxonomy of cultural and sociocultural elements, highlighting the need for culturally adaptive models as well as efforts to incorporate cultural awareness. however, even with a structured understanding of these cultural elements, capturing their intricate relationships and effectively leveraging them for one-tomany idiomatic translation remains a significant challenge. this paper introduces idiomce, an inductive graph-based approach that models the relationships between source and target idioms by leveraging complex cultural element mappings, as illustrated in figure 1 , where source is an english idiom and target are hindi idioms. using link prediction, our method facilitates one-to-many idiomatic translation while preserving cultural relevance across languages. furthermore, idiomce is adaptable, enabling the translation of unseen idioms by leveraging the inductive capabilities of gnns, effectively addressing the limitations of noisy and limited knowledge bases. our key contributions are summarized as follows: more recently, prompting techniques and chainof-thought (cot) reasoning have been investigated in large language models (llms) for idiomatic translation @xcite . idiomkb @xcite further introduced a contextual approach, using figurative meanings as additional context to improve translation quality in llms. idiomatic translation for indic languages. indic languages exhibit significant linguistic diversity and deeply rooted cultural nuances, making idiomatic translation a complex challenge. despite this, research on idiomatic translation in the indic language setting remains limited. @xcite proposes idiom identification using grammatical rule based approach.(modh and saini, 2020) proposes a identification of gujarati idioms and translation of them using contextual information. @xcite present a multilingual parallel idiom dataset encompassing seven indian languages and english. while these studies offer valuable contributions, the challenge of many-tomany idiomatic translation across indic languages remains largely under-explored. ## methodology in this section, we first present the problem statement followed by the training and inference of our methodology, which we call idiomce. ## experimental set up datasets. the initial knowledge graph (kg) construction is based on the dataset from @xcite , which provides mappings of idioms between english (en) and seven indian languages. for our study, we utilize four indic languages: tamil (ta), telugu (te), bengali (bn), and hindi (hi). additionally, we incorporate a parallel idiomatic sentence dataset from @xcite . beyond these existing resources, we also web-scraped to collect idioms in various indic languages. for evaluation, we sample 400 sentences from the magpie dataset ## results results on mixed dataset. this dataset contains a mix of idioms, both seen and unseen during training. we conducted experiments on englishto-hindi, bengali, tamil, and telugu translation directions. the results in results on additional baselines such as @xcite even with gpt-4o results, our approach shows significant improvements for both seen and unseen datasets. further details on language-specific performance can be found in the appendix in results on inter-indic languages. ## conclusion in this work, we addressed the challenges of idiomatic translation by introducing idiomce, an adaptive gnn-based approach that effectively captures the complex relationships between idiomatic expressions across languages. our method generalizes to seen and unseen idioms, improves translation quality even in smaller models, and enables translation via a pivot language through the gnn framework. experimental results across multiple indian languages demonstrate that our approach outperforms traditional static knowledge graphs and prompt-based methods, significantly improving idiomatic translation. by leveraging gpt-4 as an evaluation metric, we show that our model better preserves meaning and cultural nuances in translation. future work can extend this approach to more languages and richer contextual signals.