  Zero-shot intent classification is a vital and challenging task in dialogue
systems, which aims to deal with numerous fast-emerging unacquainted intents
without annotated training data. To obtain more satisfactory performance, the
crucial points lie in two aspects: extracting better utterance features and
strengthening the model generalization ability. In this paper, we propose a
simple yet effective meta-learning paradigm for zero-shot intent
classification. To learn better semantic representations for utterances, we
introduce a new mixture attention mechanism, which encodes the pertinent word
occurrence patterns by leveraging the distributional signature attention and
multi-layer perceptron attention simultaneously. To strengthen the transfer
ability of the model from seen classes to unseen classes, we reformulate
zero-shot intent classification with a meta-learning strategy, which trains the
model by simulating multiple zero-shot classification tasks on seen categories,
and promotes the model generalization ability with a meta-adapting procedure on
mimic unseen categories. Extensive experiments on two real-world dialogue
datasets in different languages show that our model outperforms other strong
baselines on both standard and generalized zero-shot intent classification
tasks.
