NLPExplorer

WMT - 2024

Total Papers:- 134

Total Papers accross all years:- 1520

Total Citations :- 0

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TSU HITS’s Submissions to the WMT 2024 General Machine Translation Shared Task

Vladimir Mynka | Nikolay Mikhaylovskiy |

The SETU-ADAPT Submissions to WMT 2024 Chat Translation Tasks

Universitat d’Alacant’s Submission to the WMT 2024 Shared Task on Translation into Low-Resource Languages of Spain

Aaron Galiano Jimenez | Víctor M. Sánchez-Cartagena | Juan Antonio Perez-Ortiz | Felipe Sánchez-Martínez |

Correcting FLORES Evaluation Dataset for Four African Languages

Optimising LLM-Driven Machine Translation with Context-Aware Sliding Windows

Xinye Yang | Yida Mu | Kalina Bontcheva | Xingyi Song |

Context-Aware LLM Translation System Using Conversation Summarization and Dialogue History

Mingi Sung | Seungmin Lee | Jiwon Kim | Sejoon Kim |

Cultural Adaptation of Menus: A Fine-Grained Approach

Zhonghe Zhang | Xiaoyu He | Vivek Iyer | Alexandra Birch |

Open Language Data Initiative: Advancing Low-Resource Machine Translation for Karakalpak

Mukhammadsaid Mamasaidov | Abror Shopulatov |

Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data

Mara Finkelstein | David Vilar | Markus Freitag |

Reducing Redundancy in Japanese-to-English Translation: A Multi-Pipeline Approach for Translating Repeated Elements in Japanese

Qiao Wang | Yixuan Huang | Zheng Yuan |

SRIB-NMT’s Submission to the Indic MT Shared Task in WMT 2024

Pranamya Patil | Raghavendra Hr | Aditya Raghuwanshi | Kushal Verma |

Findings of WMT 2024 Shared Task on Low-Resource Indic Languages Translation

Hybrid Distillation from RBMT and NMT: Helsinki-NLP’s Submission to the Shared Task on Translation into Low-Resource Languages of Spain

Ona De Gibert | Mikko Aulamo | Yves Scherrer | Jörg Tiedemann |

IOL Research Machine Translation Systems for WMT24 General Machine Translation Shared Task

Wenbo Zhang |

Enhancing Translation Quality: A Comparative Study of Fine-Tuning and Prompt Engineering in Dialog-Oriented Machine Translation Systems. Insights from the MULTITAN-GML Team