  End-to-end spoken language understanding (SLU) suffers from the long-tail
word problem. This paper exploits contextual biasing, a technique to improve
the speech recognition of rare words, in end-to-end SLU systems. Specifically,
a tree-constrained pointer generator (TCPGen), a powerful and efficient biasing
model component, is studied, which leverages a slot shortlist with
corresponding entities to extract biasing lists. Meanwhile, to bias the SLU
model output slot distribution, a slot probability biasing (SPB) mechanism is
proposed to calculate a slot distribution from TCPGen. Experiments on the SLURP
dataset showed consistent SLU-F1 improvements using TCPGen and SPB, especially
on unseen entities. On a new split by holding out 5 slot types for the test,
TCPGen with SPB achieved zero-shot learning with an SLU-F1 score over 50%
compared to baselines which can not deal with it. In addition to slot filling,
the intent classification accuracy was also improved.
