Example-Driven Intent Prediction with Observers

Shikib Mehri, Mihail Eric, Dilek Hakkani-Tur

Keywords: dialog, intent prediction, pre-training

Abstract: 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) example-driven training and (2) observers. Example-driven training learns to classify utterances by comparing to examples, thereby using the underlying encoder as a sentence similarity model. 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. The proposed methods attain state-of-the-art results on three intent prediction datasets (banking77, clinc150, 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.

One-sentence Summary: We achieve state-of-the-art results in intent prediction by (1) referring to examples and (2) using observers, which are tokens that attend but are not attended to.

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Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2010.08684/code)

Reviewed Version (pdf): https://openreview.net/references/pdf?id=HLL7L6kNsU