Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary
Authors: Xiaokang Liu, Jianquan Li, Jingjing Mu, Min Yang, Ruifeng Xu, Benyou Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three benchmark datasets clearly demonstrate the effectiveness of our method for open intent classification. |
| Researcher Affiliation | Collaboration | Xiaokang Liu1*, Jianquan Li2*, Jingjing Mu2, Min Yang3 , Ruifeng Xu4, Benyou Wang5,6 1 China Automotive Technology and Research Center Co., Ltd. 2 Beijing Ultrapower Software Co.,Ltd. 3 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 4 Harbin Institute of Technology, Shenzhen 5 The Chinese University of Hong Kong (Shenzhen) 6 Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, China |
| Pseudocode | No | The paper describes methods textually and with mathematical equations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | For reproducibility, we submit the code at: https://github.com/lxk00/CLAP |
| Open Datasets | Yes | We conduct extensive experiments on three publicly available benchmark datasets. BANKING (Casanueva et al. 2020) is a fine-grained dataset in the banking domain, which contains 77 intents and 13,083 customer service queries. OOS (Larson et al. 2019) is a dataset for intent classification and out-of-scope prediction. It consists of 150 intents, 22,500 in-domain queries and 1,200 out-of-domain queries. Stack Overflow (Xu et al. 2015) is a dataset released originally in Kaggle.com. |
| Dataset Splits | Yes | Dataset Class Train/Valid/Test Length (max/mean) BANKING 77 9003 / 1000 / 3080 79 / 11.91 OOS 150 15000 / 3000 / 5700 28 / 8.31 Stack Overflow 20 12000 / 2000 / 6000 41 / 9.18 |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications, or cloud instances). |
| Software Dependencies | No | The paper mentions using a 'pre-trained BERT model (base-uncased)' but does not provide specific version numbers for other key software dependencies or libraries (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | We freeze all the parameters of BERT except the last transformer layer to speed up the training process and avoid over-fitting. The number of positive samples in KCCL are in 1 to 10 and the number of negative samples M is set to be 1. λ is set to be 0.25. In the second training stage, we freeze BERT model and train the decision boundary only. The batch size is set to be 32, e in range 0.5 to 1.2, s from 0 to 0.5, η from 0 to 1. We utilize Adam to optimize the model with a learning rate of 2e-5. |