Machine Translation: 17th China Conference, CCMT 2021, Xining, China, October 8-10, 2021, Revised Selected Papers

The 10 papers presented in this volume were carefully reviewed and selected from 25 submissions, focusing on all aspects of machine translation, including preprocessing, neural machine translation models, hybrid models, evaluation methods, and post-editing.

Format: Paperback / softback
Length: 125 pages
Publication date: 30 October 2021
Publisher: Springer Verlag, Singapore

The 17th China Conference on Machine Translation (CCMT 2020) was held in Xining, China, in October 2021, and this book serves as the official proceedings of the conference. Out of a total of 25 submissions, 10 exceptional papers were chosen for inclusion in this volume, covering a wide range of topics related to machine translation. These papers delve into various aspects of the field, including preprocessing techniques, neural machine translation models, hybrid models, evaluation methods, and post-editing processes.

The conference aimed to bring together researchers, practitioners, and industry experts from around the world to discuss the latest advancements and challenges in machine translation technology. The selected papers presented at CCMT 2020 showcase the state-of-the-art research and innovative solutions being developed in the field.

Preprocessing plays a crucial role in machine translation, as it prepares the input text for translation by cleaning, tokenizing, and converting it into a format that can be easily processed by the translation models. The papers in this volume explore different preprocessing techniques, such as text cleaning, sentence segmentation, and part-of-speech tagging, and their impact on the translation quality.

Neural machine translation models have gained significant attention in recent years due to their ability to produce high-quality translations. The papers in this volume present various neural machine translation models, including recurrent neural networks, convolutional neural networks, and transformer-based models, and discuss their advantages, limitations, and applications.

Hybrid models, which combine the strengths of different translation models, have also emerged as a promising approach. The papers in this volume explore hybrid models that combine neural machine translation with statistical machine translation, machine translation with transfer learning, and other combinations, and evaluate their performance in different translation tasks.

Evaluation methods play a vital role in assessing the performance of machine translation systems. The papers in this volume present various evaluation metrics and frameworks, such as BLEU score, METEOR score, and human evaluation, and discuss their advantages, limitations, and applications.

Post-editing is a crucial step in machine translation, as it involves modifying the translated text to improve its readability and fluency. The papers in this volume explore different post-editing techniques, such as machine translation post-editing, human post-editing, and hybrid post-editing, and evaluate their effectiveness in improving the quality of machine-translated texts.

The 17th China Conference on Machine Translation (CCMT 2020) was a significant event in the field of machine translation, and this book provides a valuable resource for researchers, practitioners, and industry experts. The papers presented in this volume showcase the latest advancements and challenges in machine translation technology and offer insights into the future of this field.

In conclusion, the 17th China Conference on Machine Translation (CCMT 2020) was a successful event that brought together experts from around the world to discuss the latest advancements and challenges in machine translation technology. This book serves as a valuable resource for researchers, practitioners, and industry experts, providing a comprehensive overview of the state-of-the-art research and innovative solutions being developed in the field.

Weight: 226g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811675119
Edition number: 1st ed. 2021