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20242026
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cs.CL2026

Dolphin-CN-Dialect: Where Chinese Dialects Matter

Yangyang Meng, Huihang Zhong, Guodong Lin +6

We present Dolphin-CN-Dialect, a streaming-capable ASR model with a focus on Chinese and dialect-rich scenarios. Compared to the previous version, Dolphin-CN-Dialect introduces sub…

cs.CL2026

Do Language Models Reason Across Languages?

Yan Meng, Wafaa Mohammed, Christof Monz

The real-world information sources are inherently multilingual, which naturally raises a question about whether language models can synthesize information across languages. In this…

cs.CL2024

How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise in Machine Translation

Yan Meng, Di Wu, Christof Monz

The massive amounts of web-mined parallel data contain large amounts of noise. Semantic misalignment, as the primary source of the noise, poses a challenge for training machine tra…

cs.CL2024

How Far Can 100 Samples Go? Unlocking Overall Zero-Shot Multilingual Translation via Tiny Multi-Parallel Data

Di Wu, Shaomu Tan, Yan Meng +2

Zero-shot translation aims to translate between language pairs not seen during training in Multilingual Machine Translation (MMT) and is largely considered an open problem. A commo…

cs.CL2024

Disentangling the Roles of Target-Side Transfer and Regularization in Multilingual Machine Translation

Yan Meng, Christof Monz

Multilingual Machine Translation (MMT) benefits from knowledge transfer across different language pairs. However, improvements in one-to-many translation compared to many-to-one tr…

cs.CL20231 cited

FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation

Yan Meng, Liangming Pan, Yixin Cao +1

Humans ask follow-up questions driven by curiosity, which reflects a creative human cognitive process. We introduce the task of real-world information-seeking follow-up question ge…