Despite over a decade of research, gender bias in machine translation remains a complex challenge with no simple fix. A review of 133 studies highlights both promising trends and persistent gaps, such as narrow technical focus, English-centric approaches, and a lack of real-world grounding. These findings call for future research to move beyond past limitations and to be more user-centric, culturally diverse, and personalized.

Real-time speech interaction, serving as a fundamental interface for human-machine collaboration, holds immense potential. However, current open-source models face limitations such as high costs in vo...

Integrating speech into LLM (speech-LLM) has gaining increased attention recently. The mainstream solution is to connect a well-trained speech encoder and LLM with a neural adapter. However, the lengt...