A Multi-Task Semantic Communication System for Natural Language Processing

Recently, task-oriented semantic communication has received increasing attention due to its potential to transform the communication landscape by going beyond the Shannon paradigm. While most existing researches focus on a single task, we propose a multi-task semantic communication system for text tasks, inspired by the impressive results of multitask learning and bidirectional encoder representations from transformers (BERT). Based on BERT, the proposed system extracts semantic information from the text at the transmitter and then transmits it to the receiver, which aims to accomplish a series of different tasks. The transmitter and receiver would be trained jointly in an end-to-end manner. Compared with the traditional communication system, the proposed model is more robust to distortion caused by physical channels and exhibits better performance, especially in the low signal-to-noise ratio regime. Moreover, we investigate the relationship between the number of tasks and the required length of transmitted symbols in a multi-task setting.