883 research outputs found
Joint Learning of Word and Label Embeddings for Sequence Labelling in Spoken Language Understanding
We propose an architecture to jointly learn word and label embeddings for
slot filling in spoken language understanding. The proposed approach encodes
labels using a combination of word embeddings and straightforward word-label
association from the training data. Compared to the state-of-the-art methods,
our approach does not require label embeddings as part of the input and
therefore lends itself nicely to a wide range of model architectures. In
addition, our architecture computes contextual distances between words and
labels to avoid adding contextual windows, thus reducing memory footprint. We
validate the approach on established spoken dialogue datasets and show that it
can achieve state-of-the-art performance with much fewer trainable parameters.Comment: Accepted for publication at ASRU 201
Towards Zero-Shot Frame Semantic Parsing for Domain Scaling
State-of-the-art slot filling models for goal-oriented human/machine
conversational language understanding systems rely on deep learning methods.
While multi-task training of such models alleviates the need for large
in-domain annotated datasets, bootstrapping a semantic parsing model for a new
domain using only the semantic frame, such as the back-end API or knowledge
graph schema, is still one of the holy grail tasks of language understanding
for dialogue systems. This paper proposes a deep learning based approach that
can utilize only the slot description in context without the need for any
labeled or unlabeled in-domain examples, to quickly bootstrap a new domain. The
main idea of this paper is to leverage the encoding of the slot names and
descriptions within a multi-task deep learned slot filling model, to implicitly
align slots across domains. The proposed approach is promising for solving the
domain scaling problem and eliminating the need for any manually annotated data
or explicit schema alignment. Furthermore, our experiments on multiple domains
show that this approach results in significantly better slot-filling
performance when compared to using only in-domain data, especially in the low
data regime.Comment: 4 pages + 1 reference
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