3 accepted papers
Large-scale pre-trained neural language model has facilitated to achieve the state-of-the-art performance on Dialogue State Tracking (DST) tasks. One of the existing works models the semantic correlation between the dialogue context and (domain, slot) pair encoded by BERT and make the prediction. De…
Dialogue state tracking (DST) is a crucial component of task-oriented dialogue systems, as it aims to accurately track the user’s goals throughout the dialogue history. However, DST models struggle with new domains due to limited annotated data, leading to poor performance. To solve this key challen…
Dialogue state error correction has recently been proposed to correct wrong slot values in predicted dialogue states, thereby mitigating the error propagation problem for dialogue state tracking (DST). These approaches, though effective, are heavily intertwined with specific DST models, limiting the…