Consistent Inference for Dialogue Relation Extraction
Authors: Xinwei Long, Shuzi Niu, Yucheng Li
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two benchmark datasets show that the F1 performance improvement of the proposed method is at least 3.3% compared with SOTA. We conduct comprehensive experiments on two benchmark datasets, Dialog RE [Yu et al., 2020] and MPDD [Chen et al., 2020b], and Co In shows the 3.3% and 6.2% improvement in terms of F1 (Dialog RE) and accuracy (MPDD) than state-of-the-art models. Ablation studies prove the effectiveness of each module. |
| Researcher Affiliation | Academia | 1Institute of Software, Chinese Academy of Sciences 2University of Chinese Academy of Sciences longxinwei19@mails.ucas.ac.cn, {shuzi, yucheng}@iscas.ac.com, |
| Pseudocode | No | The paper includes an architecture diagram (Figure 2) but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source codes and pre-processed data are released in https://github.com/xinwei96/Co In dialog RE |
| Open Datasets | Yes | Datasets. (1) Dialog RE [Yu et al., 2020]. We follow the standard settings offered by the original paper, and deploy F1 score as the metric. (2) MPDD [Chen et al., 2020b]. More details of Dialog RE and processed MPDD can be found in Table 1. Source codes and pre-processed data are released in https://github.com/xinwei96/Co In dialog RE |
| Dataset Splits | Yes | Table 1: Dataset Statistics. Dialog Num. 1073 / 358 / 357, Relation Num. 4992 / 1597 / 1529. (These numbers represent train / dev / test splits, where 'dev' typically serves as the validation set). |
| Hardware Specification | Yes | Experiments are conducted on a sever with a Ge Force GTX 1080Ti GPU, 64G memory. |
| Software Dependencies | Yes | Our model was implemented by Pytorch with CUDA 11.0. |
| Experiment Setup | Yes | We adopt BERT-base architecture with the fine-tuning learning rate of 2e 5. We use a self-attention layer with dropout 0.2 and learning rate 5e 4. The number of windows K is set to 2 from {i}4 i=1. We use Adam W [Loshchilov and Hutter, 2019] as optimizer with Cosine Annealing scheduler [Loshchilov and Hutter, 2017]. The threshold τ of multi-label classifier, Trade-off parameters λ1 and λ2 are set to 0.51. |