Papers by Linh The Nguyen
PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation (2021.emnlp-main)
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| Challenge: | We present a high-quality and large-scale Vietnamese-English parallel dataset . our dataset is 2.9M pairs larger than the benchmark Vietnamese- English corpus . |
| Approach: | They present a large-scale Vietnamese-English parallel dataset with 3.02M sentence pairs . they compare strong neural baselines and well-known automatic translation engines . |
| Outcome: | The proposed dataset is 2.9M pairs larger than the benchmark Vietnamese-English corpus IWSLT15. |
Vietnamese Automatic Speech Recognition: A Revisit (2026.findings-eacl)
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| Challenge: | Existing datasets with low quality and inconsistent annotations are insufficient for high-quality models. |
| Approach: | They propose a pipeline for aggregating and preprocessing high-quality ASR datasets from diverse, potentially noisy, open-source sources. |
| Outcome: | The proposed pipeline provides a foundation for training and evaluating state-of-the-art Vietnamese ASR systems. |
PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing (2021.naacl-demos)
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| Challenge: | PhoNLP is a multi-task learning model for joint Vietnamese part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing. |
| Approach: | They propose a multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing that fine-tunes the pre-trained Vietnamese language model PhoBERT for each task independently. |
| Outcome: | The proposed model outperforms a single-task learning approach that fine-tunes the pre-trained Vietnamese language model PhoBERT for each task independently. |
Zero-Shot Text-to-Speech for Vietnamese (2025.acl-short)
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| Challenge: | Text-to-speech (TTS) synthesis has seen significant advancements in recent years. |
| Approach: | They propose to use PhoAudiobook to curated 941 hours of high-quality audio for Vietnamese text-to-speech models. |
| Outcome: | The proposed model improves on VALL-E, VoiceCraft, and XTTS-V2 models, highlighting their robustness in handling diverse linguistic contexts. |
Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings (P19-1)
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| Challenge: | Existing models for implicit discourse relation recognition lack the ability to accurately map connectives into discourse relations. |
| Approach: | They propose a multi-task learning framework where relations and connectives are simultaneously predicted and leveraged to transfer knowledge between the two prediction tasks. |
| Outcome: | The proposed framework yields state-of-the-art performance on several settings of the Penn Discourse Treebank dataset. |