  While large pre-trained language models accumulate a lot of knowledge in
their parameters, it has been demonstrated that augmenting it with
non-parametric retrieval-based memory has a number of benefits from accuracy
improvements to data efficiency for knowledge-focused tasks, such as question
answering. In this paper, we are applying retrieval-based modeling ideas to the
problem of multi-domain task-oriented semantic parsing for conversational
assistants. Our approach, RetroNLU, extends a sequence-to-sequence model
architecture with a retrieval component, used to fetch existing similar
examples and provide them as an additional input to the model. In particular,
we analyze two settings, where we augment an input with (a) retrieved nearest
neighbor utterances (utterance-nn), and (b) ground-truth semantic parses of
nearest neighbor utterances (semparse-nn). Our technique outperforms the
baseline method by 1.5% absolute macro-F1, especially at the low resource
setting, matching the baseline model accuracy with only 40% of the data.
Furthermore, we analyze the nearest neighbor retrieval component's quality,
model sensitivity and break down the performance for semantic parses of
different utterance complexity.
