Cross-Modal Knowledge Distillation for heritage language revitalization programs during mission-critical recovery windows
It started with a dying language and a broken model. I was sitting in my home office, surrounded by stacks of linguistic documentation from the Ainu language—one of Japan's indigenous languages with only a handful of fluent speakers remaining. I had spent the previous six months building a neural machine translation system to help revitalization efforts, but the results were disappointing. My model had access to only 3,000 parallel sentences, a pittance for any modern NMT system. The translations were garbled, the morphology was inconsistent, and the model's confidence scores were dangerously overconfident.
What I discovered next changed my entire research trajectory. While exploring the intersection of multimodal learning and low-resource language processing, I realized that the Ainu language documentation wasn't just text—it contained thousands of hours of audio recordings, traditional songs, oral histories, and even video documentation of cultural practices. The problem wasn't a lack of data; it was a lack of cross-modal data utilization. The text corpus was small, but the audio and visual corpora were substantially richer. This realization led me down a rabbit hole of cross-modal knowledge distillation that would eventually form the backbone of what I now call "mission-critical recovery windows" for heritage language programs.
