Papers by Pratik Rakesh Singh
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. |
Graph-Based Phonetic Error Correction of Noisy ASR (2026.acl-industry)
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| Challenge: | Automatic speech recognition systems produce residual transcription errors that affect semantically critical tokens. |
| Approach: | They propose a phonetic-based algorithm that combines phonetic graph modeling with contextual language understanding to improve automatic speech recognition. |
| Outcome: | The proposed framework decouples phonetic reasoning from contextual semantic selection and improves accuracy. |
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. |