Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction
Authors: Shaowei Chen, Yu Wang, Jie Liu, Yuelin Wang12666-12674
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To verify the effectiveness of our approach, we conduct extensive experiments on four benchmark datasets. The experimental results demonstrate that BMRC achieves state-of-the-art performances. |
| Researcher Affiliation | Collaboration | 1College of Artificial Intelligence, Nankai University, Tianjin, China 2Cloopen Research, Beijing, China |
| Pseudocode | No | The paper describes the model architecture and equations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/NKU-IIPLab/BMRC. |
| Open Datasets | Yes | we conduct experiments on four benchmark datasets5 from the Sem Eval ABSA Challenges (Pontiki et al. 2014, 2015, 2016) and list the statistics of these datasets in Table 1. 5https://github.com/xuuuluuu/Sem Eval-Triplet-data |
| Dataset Splits | Yes | Datasets Train Dev Test #S #T #S #T #S #T 14-Lap (Pontiki et al. 2014) 920 1265 228 337 339 490... According to the triplet extraction F1score on the development sets, the threshold δ is manually tuned to 0.8 in bound [0, 1) with step size set to 0.1. |
| Hardware Specification | Yes | We run our model on a Tesla V100 GPU and train our model for 40 epochs in about 1.5h. |
| Software Dependencies | No | The paper mentions adopting 'BERT-base' but does not specify other software dependencies with version numbers (e.g., Python, PyTorch versions, etc.). |
| Experiment Setup | Yes | During training, we use Adam W (Loshchilov and Hutter 2017) for optimization with weight decay 0.01 and warmup rate 0.1. The learning rate for training classifiers and the fine-tuning rate for BERT are set to 1e-3 and 1e-5 respectively. Meanwhile, we set batch size to 4 and dropout rate to 0.1. |