Name
Title | ||
|---|---|---|
Attention-Based Rnn Model For Joint Extraction Of Intent And Word Slot Based On A Tagging Strategy |
Abstract | ||
|---|---|---|
In this paper, we proposed an attention-based recurrent neural network model based on a tagging strategy for intent detection and word slot extraction. Unlike other joint models dividing the joint task into two sub-models by sharing parameters, we explore a tagging strategy to incorporate the intent detection task and word slot extraction task in a sequence labeling model. We implemented experiments on a public dataset and the results show that the tagging strategy methods outperform most of the existing pipelined and joint methods. Our tagging strategy model obtained 97.65% accuracy rate on intent detection task and 95.15% F1 score on word slot extraction task. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01424-7_18 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT III |
Keywords | Field | DocType |
Intent detection, Word slot extraction, Joint model, Attention mechanism, Tagging strategy | F1 score,Sequence labeling,Pattern recognition,Computer science,Recurrent neural network,Artificial intelligence | Conference |
Volume | ISSN | Citations |
11141 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 10 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongjie Zhang | 1 | 8 | 0.87 |
| Zheng Fang | 2 | 0 | 0.68 |
| Ya-nan Cao | 3 | 131 | 19.42 |
| Yanbing Liu | 4 | 19 | 12.33 |
| Chen, X. | 5 | 5 | 1.48 |
| Jianlong Tan | 6 | 132 | 22.14 |