<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In task-based dialogue system, the key of the natural language understanding module is intent detection and slot filling. At this stage, Joint modeling of intention detection and slot filling tasks has become the mainstream and achieved good results. In order to investigate the correlation between intention detection and slot filling tasks, Joint model of intention detection and slot filling based on attention mechanism in three dimensions: one-way modeling from intention to slot, Unidirectional modeling from slot to intention and bidirectional modeling from intention to slot Separately. And experiments were conducted using the Chinese dataset CAIS, and the results showed three evaluation results for time slot F1.The intention accuracy and overall accuracy of joint models for intention detection and filling gaps are usually higher than those of unidirectional models.</p>
</abstract>

In task-based dialogue system, the key of the natural language understanding module is intent detection and slot filling. At this stage, Joint modeling of intention detection and slot filling tasks has become the mainstream and achieved good results. In order to investigate the correlation between intention detection and slot filling tasks, Joint model of intention detection and slot filling based on attention mechanism in three dimensions: one-way modeling from intention to slot, Unidirectional modeling from slot to intention and bidirectional modeling from intention to slot Separately. And experiments were conducted using the Chinese dataset CAIS, and the results showed three evaluation results for time slot F1.The intention accuracy and overall accuracy of joint models for intention detection and filling gaps are usually higher than those of unidirectional models.

Research on Joint Modeling of Intent Detection and Slot Filling

School of Computer Science and Engineering

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Research on Joint Modeling of Intent Detection and Slot Filling"}

In task-based dialogue system, the key of the natural language understanding module is intent detection and slot filling. At this stage, Joint modeling of...

Experiment	Model	CAIS
Experiment	Model	Slot F1	Acc	Sent-acc
1	Sloted Gated	81.8^a	94.3	80.5
2	Stack-Propagation	87.8	94.7	84.7
3	SF-ID Network	84.9	94.5	82.4
4	CM-Net	86.2	94.6	84.6
5	Co-Interactive Transformer	88.6	95.2	86.2
6	Our Model	89.2	96.3	87.9

Model	CAIS
Model	Slot F1	Acc	Sent-acc
Intent➡Slot	88.4	95.8	85.5
Slot➡Intent	88.8	95.6	85.8
Our Model	89.2	96.3	87.9

Research on Joint Modeling of Intent Detection and Slot Filling

Publicado en línea: 16 ago 2023

Páginas: 81 - 88

DOI: https://doi.org/10.2478/ijanmc-2023-0059

Palabras clave
Intent Detection, Slot Filling, Multi-head Attention Mechanism

© 2023 Dan Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

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Figure 7.

Model Results of Chinese Dataset CAIS

Results of Model in Chinese Dataset CAIS

Research on Joint Modeling of Intent Detection and Slot Filling

Publicado en línea: 16 ago 2023

Páginas: 81 - 88

DOI: https://doi.org/10.2478/ijanmc-2023-0059

Palabras claveIntent Detection, Slot Filling, Multi-head Attention Mechanism

© 2023 Dan Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Model Results of Chinese Dataset CAIS

Results of Model in Chinese Dataset CAIS

Palabras clave
Intent Detection, Slot Filling, Multi-head Attention Mechanism