Papers by Mohammadi Zaki

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

Copied to clipboard

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)

Copied to clipboard

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)

Copied to clipboard

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.
Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning (2024.findings-naacl)

Copied to clipboard

Challenge: Traditional Automatic Video Dubbing (AVD) pipelines use isometric-NMT algorithms to regulate the length of the output text.
Approach: They propose an isometric-NMT system that regulates the length of the output text . they propose a phoneme Count Compliance score to measure length compliance .
Outcome: The proposed approach improves phoneme count compliance scores by 36% compared to state-of-the-art models in English-Hindi language pairs.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations