On the one hand, the VRALSTM models (trained from Scratch or trained adapting model from Source domains) produce the outputs with a diverse range of error types, including missing, misplaced, redundant, wrong slots, or even spelling mistake information, leading to a very high of the slot error rate ERR score.
the VRALSTM from Scratch tends to make repeated slots and also many of the missing slots in the generated outputs since the training data may inadequate for the model to generally Table 4: Comparison of top Laptop responses generated for different scenarios by adaptation training VRALSTM (denoted by ) and VDANLG (denoted by ) models from Source domains, and by training VRALSTM from Scratch.