Joint model for intent and entity recognition
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Abstract (arXiv)
The semantic understanding of natural dialogues composes of several parts. Some of them, like intent classification and entity detection, have a crucial role in deciding the next steps in handling user input. Handling each task as an individual problem can be wasting of training resources, and also each problem can benefit from each other. This paper tackles these problems as one. Our new model, which combine intent and entity recognition into one system, is achieving better metrics in both tasks with lower training requirements than solving each task separately. We also optimize the model based on the inputs.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 13 references that are in the map. First posted on arXiv 7 Sep 2021.
Builds on
9 of 13 references.
References · 9
- 2019ERNIE: Enhanced Language Representation with Informative Entitiescited by 525
- 2018Learning Word Vectors for 157 Languagescited by 279
- 2019BERT for Joint Intent Classification and Slot Fillingcited by 195
- 2016Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Fillingcited by 137
- 2019A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understandingcited by 106
- 2018A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Fillingcited by 53
- 2016Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Fillingcited by 25
- 2015Recurrent Neural Networks with External Memory for Language Understandingcited by 6
- 2017Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networkscited by 5
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Citing papers per year: 2021: 1.
1 paper.