A key challenge of dialog systems research is to effectively and efficiently
adapt to new domains. A scalable paradigm for adaptation necessitates the
development of generalizable models that perform well in few-shot settings. In
this paper, we focus on the intent classification problem which aims to
identify user intents given utterances addressed to the dialog system. We
propose two approaches for improving the generalizability of utterance
classification models: (1) observers and (2) example-driven training. Prior
work has shown that BERT-like models tend to attribute a significant amount of
attention to the [CLS] token, which we hypothesize results in diluted
representations. Observers are tokens that are not attended to, and are an
alternative to the [CLS] token as a semantic representation of utterances.
Example-driven training learns to classify utterances by comparing to examples,
thereby using the underlying encoder as a sentence similarity model. These
methods are complementary; improving the representation through observers
allows the example-driven model to better measure sentence similarities. When
combined, the proposed methods attain state-of-the-art results on three intent
prediction datasets (\textsc{banking77}, \textsc{clinc150}, \textsc{hwu64}) in
both the full data and few-shot (10 examples per intent) settings. Furthermore,
we demonstrate that the proposed approach can transfer to new intents and
across datasets without any additional training.

本论文旨在提出一种有效的适应新领域的可扩展模型，该模型使用观察者和基于示例的训练方法提高了话语分类模型的概括能力，并实现在银行，CLINC150 和 HWU64 上进行的语言意图识别中，采用以上方法获得了最先进的结果。