Novel Slot Detection With an Incremental Setting

Chen Liang, Hongliang Li, Changhao Guan, Qingbin Liu, Jian Liu, Jinan Xu, Zhe Zhao

Submission Type: Regular Long Paper

Submission Track: Information Extraction

Submission Track 2: Information Extraction

Keywords: dialog system; novel slot detection; incremental learning

Abstract:

Current dialogue systems face diverse user requests and rapid change domains, making quickly adapt to scenarios with previous unseen slot types become a major challenge.
Recently, researchers have introduced novel slot detection (NSD) to discover potential new types. However, dialogue system with NSD does not bring practical improvements due to the system still cannot handle novel slots in subsequent interactions.
In this paper, we define incremental novel slot detection (INSD), which separates the dialogue system to deal with novel types as two major phrases: 1) model discovers unknown slots, 2) training model to possess the capability to handle new classes.
We provide an effective model to extract novel slots with set prediction strategy and propose a query-enhanced approach to overcome catastrophic forgetting during the process of INSD.
We construct two INSD datasets to evaluate our method and experimental results show that our approach exhibits superior performance.

Submission Number: 3931