  In contrast to conventional pipeline Spoken Language Understanding (SLU)
which consists of automatic speech recognition (ASR) and natural language
understanding (NLU), end-to-end SLU infers the semantic meaning directly from
speech and overcomes the error propagation caused by ASR. End-to-end slot
filling (SF) from speech is an essential component of end-to-end SLU, and is
usually regarded as a sequence-to-sequence generation problem, heavily relied
on the performance of language model of ASR. However, it is hard to generate a
correct slot when the slot is out-of-vovabulary (OOV) in training data,
especially when a slot is an anti-linguistic entity without grammatical rule.
Inspired by object detection in computer vision that is to detect the object
from an image, we consider SF as the task of slot detection from speech. In
this paper, we formulate the SF task as a matching task and propose an
end-to-end knowledge-based SF model, named Speech-to-Slot (Speech2Slot), to
leverage knowledge to detect the boundary of a slot from the speech. We also
release a large-scale dataset of Chinese speech for slot filling, containing
more than 830,000 samples. The experiments show that our approach is markedly
superior to the conventional pipeline SLU approach, and outperforms the
state-of-the-art end-to-end SF approach with 12.51% accuracy improvement.
