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arXiv · 1904.08524

Towards Open Intent Discovery for Conversational Text

Nikhita Vedula·Nedim Lipka·Pranav Maneriker·Srinivasan Parthasarathy
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Abstract

Detecting and identifying user intent from text, both written and spoken, plays an important role in modelling and understand dialogs. Existing research for intent discovery model it as a classification task with a predefined set of known categories. To generailze beyond these preexisting classes, we define a new task of \textit{open intent discovery}. We investigate how intent can be generalized to those not seen during training. To this end, we propose a two-stage approach to this task - predicting whether an utterance contains an intent, and then tagging the intent in the input utterance. Our model consists of a bidirectional LSTM with a CRF on top to capture contextual semantics, subject to some constraints. Self-attention is used to learn long distance dependencies. Further, we adapt an adversarial training approach to improve robustness and perforamce across domains. We also present a dataset of 25k real-life utterances that have been labelled via crowd sourcing. Our experiments across different domains and real-world datasets show the effectiveness of our approach, with less than 100 annotated examples needed per unique domain to recognize diverse intents. The approach outperforms state-of-the-art baselines by 5-15% F1 score points.

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Nikhita Vedula, Nedim Lipka, Pranav Maneriker, Srinivasan Parthasarathy. 2019-04-17. Towards Open Intent Discovery for Conversational Text. https://arxiv.org/abs/1904.08524

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