Open-vocabulary slots, such as file name, album name, or schedule title, significantly degrade the performance of neural-based slot filling models since these slots can take on values from a virtually unlimited set and have no semantic restriction nor a length limit.
Detecting out-of-domain (OOD) intents is crucial for the deployed task-oriented dialogue system.
In this paper, we propose an adversarial disentangled debiasing model to dynamically decouple social bias attributes from the intermediate representations trained on the main task.
Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set.
Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system.
Neural-based context-aware models for slot tagging have achieved state-of-the-art performance.
In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner.