Code and data for "Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category Detection" (Findings of EMNLP 2022)
- In this paper, we propose a Label-Driven Denoising Framework (LDF) to alleviate the noise problems for the FS-ACD task.
- Label-Driven Denoising Framework contains a label-guided attention strategy to filter noisy words and generate a representative prototype for each aspect, and a label-weighted contrastive loss to avoid generating similar prototypes for semantically-close aspect categories.
+ python 3.7
+ tensorflow 2.4.0
+ keras 2.4.3
+ sklearn 0.0
+ numpy 1.19.5please download the glove.6B.50d embedding (Link) and put it into word_embedding folder
- you can choose one or multiple methods at one time in the model_list
e.g., model_list = [None, 'AWATT+LAS', 'LDF_AWATT']
# code: corresponding model:
# None the original AWATT model
# 'AWATT_LAS' AWATT+LAS
# 'AWATT_LCL' AWATT+LCL
# 'AWATT_SCL' AWATT+SCL
# 'LDF_AWATT' LDF-AWATT
# 'HATT' the original HATT model
# 'HATT_LAS' HATT+LAS
# 'HATT_LCL' HATT+LCL
# 'HATT_SCL' HATT+SCL
# 'LDF-HATT' LDF-HATT- you can choose one or multiple datasets at one time in the dataset_list
e.g., dataset_list = ['FewAsp', 'FewAsp(single)', 'FewAsp(multi)']- you can choose one or multiple configs at one time in the config_list
e.g., config_list = [[2, 5, 5], [1, 5, 10], [1, 10, 5], [1, 10, 10]]
# [2, 5, 5] stands for: two(2) '5'-way-'5'-shot meta-tasks for two batch-size
# [1, 5, 10] stands for: one(1) '5'-way-'10'-shot meta-task for one batch-size
# [1, 10, 5] stands for: one(1) '10'-way-'5'-shot meta-task for one batch-size
# [1, 10, 10] stands for: one(1) '10'-way-'10'-shot meta-task for one batch-size- You can use the folowing command to train and test LDF on the FS-ACD task:
python train_and_test.py- The final results can be saved in the excel file you specified:
e.g., pd.DataFrame(result_list).to_excel("/data1/zhaof/LDF/" + 'result.xlsx')
If the code is used in your research, please cite our paper.
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