Attention Modulation for Zero-Shot Cross-Domain Dialogue State Tracking

International audienceDialog state tracking (DST) is a core step for task-oriented dialogue systems aiming to track the user's current goal during a dialogue. Recently a special focus has been put on applying existing DST models to new domains, in other words performing zero-shot crossdomain transfer. While recent state-of-theart models leverage large pre-trained language models, no work has been made on understanding and improving the results of first-developed zero-shot models like SUMBT. In this paper, we thus propose to improve SUMBT zero-shot results on MultiWOZ by using attention modulation during inference. This method improves SUMBT zero-shot results significantly on two domains and does not worsen the initial performance with the significant advantage of needing no additional training