Machine Translation (eBook, PDF)

20th China Conference, CCMT 2024, Xiamen, China, November 8-10, 2024, Proceedings
Redaktion: He, Zhongjun; Chen, Yidong

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 20th China Conference on Machine Translation, CCMT 2024, which took place in Xiamen, China, during November 8-10, 2024.

The 13 full papers included in this book were carefully reviewed and selected from 52 submissions. They were organized in topical sections as follows: robustness and efficiency of translation models; low-resource machine translation; quality estimation; large language modes for machine translation; multi-modal translation; and machine translation evaluation.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 176
• Erscheinungstermin: 19. Februar 2025
• Englisch
• ISBN-13: 9789819622924
• Artikelnr.: 73420686

Inhaltsangabe

.- Robustness and Efficiency of Translation Models.
.- A Data-Efficient Nearest-Neighbor Language Model via Lightweight Nets.
.- Extend Adversarial Policy Against Neural Machine Translation via Unknown Token.
.- Low-resource Machine Translation.
.- Evaluating the Translation Performance of Multilingual Large Language Models: a Case Study on Southeast Asian Language.
.- Quality Estimation.
.- Critical Error Detection based on Anchors Test.
.- Large Language Modes for Machine Translation.
.- Enhancing Machine Translation Across Multiple Domains and Languages with Large Language Models.
.- Incorporating Terminology Knowledge into Large Language Model for Domain-specific Machine Translation.
.- Multi-modal Translation.
.- Joint Multi-modal Modeling for Speech-to-Text Translation as Multilingual Neural Machine Translation.
.- Machine Translation Evaluation.
.- CCMT2024 Tibetan-Chinese Machine Translation Evaluation Technical Report.
.- HW-TSC's Submission to the CCMT 2024 Machine Translation Task.
.- ISTIC's Neural Machine Translation Systems for CCMT' 2024.
.- Lan-Bridge's Submission to CCMT 2024 Translation Evaluation Task.
.- Technical Report of OPPO's Machine Translation Systems for CCMT 2024.
.- Xihong's Submission to CCMT 2024: Human-in-the-Loop Data Augmentation for Low-Resource Tibetan-Chinese NMT.