CRUISE: Cold-Start New Skill Development via Iterative Utterance Generation

By Yilin Shen, Avik Ray, Abhishek Patel, Hongxia Jin. 2018

9 citations recorded by the source corpus.

We present a system, CRUISE, that guides ordinary software developers to build a high quality natural language understanding (NLU) engine from scratch. This is the fundamental step of building a new skill in personal assistants. Unlike existing solutions that require either developers or crowdsourcing to manually generate and annotate a large number of utterances, we design a hybrid rule-based and data-driven approach with the capability to iteratively generate more and more utterances. Our system only requires light human workload to iteratively prune incorrect utterances. CRUISE outputs a well trained NLU engine and a large scale annotated utterance corpus that third parties can use to develop their custom skills. Using both benchmark dataset and custom datasets we collected in real-world settings, we validate the high quality of CRUISE generated utterances via both competitive NLU performance and human evaluation. We also show the largely reduced human workload in terms of both cognitive load and human pruning time consumption.

Subfields: Artificial Intelligence

How researchers see this paper

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In Artificial Intelligence

1. Xiangyu Zeng
2. Hongxia Jin
3. Yilin Shen
4. Yixuan Li
5. Zixuan Ke

Citations in the corpus

• A Progressive Model to Enable Continual Learning for Semantic Slot Filling (2019, background): “On the other hand, (Ray et al., 2018; Shen et al., 2018b) attempt to personalize the slot filling model.”