Memory-Assistant Collaborative Language Understanding for Artificial Intelligence of Things

Abstract

Artificial intelligence shows promising efforts in collaborating the language models with the artificial intelligence of things (AIoT), promoting the edging intelligence on natural language understanding. To adapt to the limited computational resources in AIoT, the large language models (e.g., transformer) are compressed into light-weight models, which always results in poor feature representation and unsatisfactory performance on downstream tasks, especially on those low-resource language understanding tasks. To address the above issues, we propose a method named memory-assistant multi-task learning (MAMT), where an auxiliary memory module is introduced to promote multitask learning (MT), which serves as a surrogate of target domain representation and performs instance-level weighted MT. More importantly, our MAMT module is in a plug-and-play fashion. Thus, researchers can plug in it to conduct collaborative training and plug it out for AIoT model inference without extra computation burdens. Experiments demonstrate that MAMT significantly improves the performance of light-weight transformer models and show its superiority over the state-of-the-arts on eight GLUE subtasks.