Buch, Englisch, Band 2365, 176 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 300 g
20th China Conference, CCMT 2024, Xiamen, China, November 8-10, 2024, Proceedings
Buch, Englisch, Band 2365, 176 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 300 g
Reihe: Communications in Computer and Information Science
ISBN: 978-981-962291-7
Verlag: Springer Nature Singapore
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Geistes- und Sozialwissenschaften
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie
- Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
- Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken
Weitere Infos & Material
.- 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.
