Abstract

A spoken language understanding (SLU) system includes two main tasks, slot filling (SF) and intent detection (ID). The joint model for the two tasks is becoming a tendency in SLU. But the bi-directional interrelated connections between the intent and slots are not established in the existing joint models. In this paper, we propose a novel bi-directional interrelated model for joint intent detection and slot filling. We introduce an SF-ID network to establish direct connections for the two tasks to help them promote each other mutually. Besides, we design an entirely new iteration mechanism inside the SF-ID network to enhance the bi-directional interrelated connections. The experimental results show that the relative improvement in the sentence-level semantic frame accuracy of our model is 3.79% and 5.42% on ATIS and Snips datasets, respectively, compared to the state-of-the-art model.

Highlights

  • Spoken language understanding plays an important role in spoken dialogue system

  • General approaches such as support vector machine (SVM) (Haffner et al, 2003) and recurrent neural network (RNN) (Lai et al, 2015) can be applied

  • This paper proposes an slot filling (SF)-intent detection (ID) network which consists of an SF subnet and an ID subnet

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Summary

Introduction

Spoken language understanding plays an important role in spoken dialogue system. SLU aims at extracting the semantics from user utterances. Intent detection is seen as a semantic classification problem to predict the intent label General approaches such as support vector machine (SVM) (Haffner et al, 2003) and recurrent neural network (RNN) (Lai et al, 2015) can be applied. Goo et al (2018) proposed a slotgated model which applies the intent information to slot filling task and achieved superior performance. The SF subnet applies intent information to slot filling task while the ID subnet uses slot information in intent detection task In this case, the bi-directional interrelated connections for the two tasks can be established. Our contributions are summarized as follows: 1) We propose an SF-ID network to establish the interrelated mechanism for slot filling and intent detection tasks. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5467–5471 Florence, Italy, July 28 - August 2, 2019. c 2019 Association for Computational Linguistics

Proposed Approaches
Integration of Context
SF-ID Network
SF-First Mode
ID-First Mode
CRF layer
Experiment
Findings
Conclusion
Full Text
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