Papers by Kritarth Prasad

2 papers
Graph-Assisted Culturally Adaptable Idiomatic Translation for Indic languages (2025.findings-acl)

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Challenge: a single source idiom can have multiple target-language equivalents depending on cultural references and contextual variations.
Approach: They propose an adaptive graph neural network-based method that learns intricate mappings between idiomatic expressions and generalizes to both seen and unseen nodes during training.
Outcome: The proposed method improves translation quality even in resource-constrained settings, facilitating improved idiomatic translation in smaller models.
Faster Machine Translation Ensembling with Reinforcement Learning and Competitive Correction (2025.findings-naacl)

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Challenge: Recent approaches to ensembling neural machine translation models require inference across all candidate models, leading to significant computational overhead.
Approach: They propose a reinforcement learning-based strategy that improves the CSB by selecting a small, fixed number of candidates and identifying optimal groups to pass to the fusion block for each input sentence.
Outcome: The proposed approach improves the CSB by selecting a small, fixed number of candidates and identifying optimal groups to pass to the fusion block for each input sentence.

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