  Recurrent neural network (RNN) based joint intent classification and slot
tagging models have achieved tremendous success in recent years for building
spoken language understanding and dialog systems. However, these models suffer
from poor performance for slots which often encounter large semantic
variability in slot values after deployment (e.g. message texts, partial
movie/artist names). While greedy delexicalization of slots in the input
utterance via substring matching can partly improve performance, it often
produces incorrect input. Moreover, such techniques cannot delexicalize slots
with out-of-vocabulary slot values not seen at training. In this paper, we
propose a novel iterative delexicalization algorithm, which can accurately
delexicalize the input, even with out-of-vocabulary slot values. Based on model
confidence of the current delexicalized input, our algorithm improves
delexicalization in every iteration to converge to the best input having the
highest confidence. We show on benchmark and in-house datasets that our
algorithm can greatly improve parsing performance for RNN based models,
especially for out-of-distribution slot values.
