View Translation
View translation encompasses the automated conversion of information between different modalities (e.g., text, speech, images, sign language) and languages, aiming to bridge communication gaps across diverse forms of expression. Current research emphasizes improving translation accuracy and efficiency using large language models (LLMs), exploring techniques like contrastive preference optimization, attention mechanism refinements, and multi-source pivoting, often within specific architectures such as transformers and Conformers. This field is crucial for advancing multilingual natural language processing, enabling broader access to information and facilitating cross-cultural communication in various applications, including healthcare, education, and cybersecurity.
Papers
Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards
Baban Gain, Ramakrishna Appicharla, Soumya Chennabasavaraj, Nikesh Garera, Asif Ekbal, Muthusamy Chelliah
Leveraging Timestamp Information for Serialized Joint Streaming Recognition and Translation
Sara Papi, Peidong Wang, Junkun Chen, Jian Xue, Naoyuki Kanda, Jinyu Li, Yashesh Gaur
Rethinking Word-Level Auto-Completion in Computer-Aided Translation
Xingyu Chen, Lemao Liu, Guoping Huang, Zhirui Zhang, Mingming Yang, Shuming Shi, Rui Wang