Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning

Understand

We present a multi-task learning framework to enable the training of one universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging, and utterance segmentation in a simple deep recurrent setting.

• We show that these tasks provide positive inductive biases to each other with the optimal contribution of each one relying on the severity of the noise from the task.
• Our live multi-task model outperforms similar individual tasks, delivers competitive performance, and is beneficial for future use in conversational agents in psychiatric treatment.