Towards Robust Personalized Dialogue Generation via Order-Insensitive
  Representation Regularization
Liang Chen Hongru Wang Yang Deng Wai-Chung Kwan Zezhong Wang Kam-Fai Wong
Generating persona consistent dialogue response is important for developing
an intelligent conversational agent. Recent works typically fine-tune
large-scale pre-trained models on this task by concatenating persona texts and
dialogue history as a single input sequence to generate the target response.
While simple and effective, our analysis shows that this popular practice is
seriously affected by order sensitivity where different input orders of persona
sentences significantly impact the quality and consistency of generated
response, resulting in severe performance fluctuations (i.e., 29.4% on GPT2 and
83.2% on BART). To mitigate the order sensitivity problem, we propose a
model-agnostic framework, ORder Insensitive Generation (ORIG), which enables
dialogue models to learn robust representation under different persona orders
and improve the consistency of response generation. Experiments on the
Persona-Chat dataset justify the effectiveness and superiority of our method
with two dominant pre-trained models (GPT2 and BART).