Papers by Abudurexiti Reheman
Exploiting Target Language Data for Neural Machine Translation Beyond Back Translation (2024.findings-acl)
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| Challenge: | Neural Machine Translation (NMT) encounters challenges when translating in new domains and low-resource languages. |
| Approach: | They propose a variant of k-nearest neighbor machine translation that utilizes target language data by constructing a pseudo datastore. |
| Outcome: | The proposed method exhibits strong domain adaptation capability in both high-resource and low-resourced machine translation. |
Augmenting Large Language Model Translators via Translation Memories (2023.findings-acl)
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Yongyu Mu, Abudurexiti Reheman, Zhiquan Cao, Yuchun Fan, Bei Li, Yinqiao Li, Tong Xiao, Chunliang Zhang, Jingbo Zhu
| Challenge: | Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models. |
| Approach: | They propose to use translation memories (TMs) as prompts to prompt large language models (LLMs) they find that the ability of LLMs to "understand" prompts is helpful . |
| Outcome: | The results are comparable to state-of-the-art NMT systems with bilingual data and are tuned on downstream tasks. |
Enhancing Neural Machine Translation Through Target Language Data: A kNN-LM Approach for Domain Adaptation (2025.acl-long)
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Abudurexiti Reheman, Hongyu Liu, Junhao Ruan, Abudukeyumu Abudula, Yingfeng Luo, Tong Xiao, JingBo Zhu
| Challenge: | Neural machine translation (NMT) has made significant progress in recent years, yet often suffers from translating in new domains, which is called domain adaptation. |
| Approach: | They propose a method that leverages semantically similar target language sentences in the kNN framework and generates a probability distribution over these sentences during decoding. |
| Outcome: | The proposed method generates a probability distribution over similar target language sentences and then interpolates with the model’s distribution. |
Synergizing Semantic Anchors and Ordinal Smoothed Cross-Entropy for Speech Fluency Classification (2026.findings-acl)
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Mulati Kahaer, Sirajahmat Ruzmamat, XuDong Pang, Subinuer Maimaitituerxun, Zaokere Kadeer, Abudurexiti Reheman, Wenwen Lu, Panpan Zheng, Aishan Wumaier
| Challenge: | Existing methods fail to bridge the semantic gap between static expert priors and dynamic temporal representations while overlooking the inherent ordinal nature of fluency scores. |
| Approach: | They propose a set of expert features targeting fluency disruptions and rhythmic regularity to provide explicit linguistic priors. |
| Outcome: | The proposed model outperforms baseline models in both macroscopic and microscopic speech flow trends and local anomalies. |