  Neural models have yielded state-of-the-art results in deciphering spoken
language understanding (SLU) problems; however, these models require a
significant amount of domain-specific labeled examples for training, which is
prohibitively expensive. While pre-trained language models like BERT have been
shown to capture a massive amount of knowledge by learning from unlabeled
corpora and solve SLU using fewer labeled examples for adaption, the encoding
of knowledge is implicit and agnostic to downstream tasks. Such encoding
results in model inefficiencies in parameter usage: an entirely new model is
required for every domain. To address these challenges, we introduce a novel
SLU framework, comprising a conversational language modeling (CLM) pre-training
task and a light encoder architecture. The CLM pre-training enables networks to
capture the representation of the language in conversation style with the
presence of ASR errors. The light encoder architecture separates the shared
pre-trained networks from the mappings of generally encoded knowledge to
specific domains of SLU, allowing for the domain adaptation to be performed
solely at the light encoder and thus increasing efficiency. With the framework,
we match the performance of state-of-the-art SLU results on Alexa internal
datasets and on two public ones (ATIS, SNIPS), adding only 4.4% parameters per
task.
