Evidence-Aware Hierarchical Interactive Attention Networks for Explainable Claim Verification
Authors: Lianwei Wu, Yuan Rao, Xiong Yang, Wanzhen Wang, Ambreen Nazir
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two datasets demonstrate that EHIAN not only achieves the state-of-the-art performance but also secures effective evidence to explain the results. |
| Researcher Affiliation | Academia | Lianwei Wu , Yuan Rao , Xiong Yang , Wanzhen Wang and Ambreen Nazir Lab of Social Intelligence and Complexity Data Processing, School of Software Engineering, Xi an Jiaotong University, China Shannxi Joint Key Laboratory for Artifact Intelligence(Sub-Lab of Xi an Jiaotong University), China Research Institute of Xi an Jiaotong University, Shenzhen, China {stayhungry, youngpanda, kelyin0417, ambreen.nazir}@stu.xjtu.edu.cn, raoyuan@mail.xjtu.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to its own source code (e.g., a specific repository link or an explicit code release statement). |
| Open Datasets | Yes | We use two public fact-checking datasets, i.e., Snopes and Politi Fact provided by Popat et al. [2018] for evaluation. |
| Dataset Splits | Yes | We hold out 10% of all data as validation data for parameter tuning, and conduct 5-fold cross-validation on the remaining 90% of the data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like "pre-trained BERT-base model [Devlin et al., 2019]", "Adam optimizer [Kingma and Ba, 2014]", "Tensorflow4", and "Theano5". However, it does not provide specific version numbers for the software dependencies used in their implementation beyond the general references to TensorFlow and Theano. |
| Experiment Setup | Yes | For parameter configurations, we apply the pre-trained BERTbase model [Devlin et al., 2019] to initialize our token embeddings and their length is set to 300. In self-attention networks, attention heads and blocks are set to 6 and 2 respectively, and the dropout of multi-head attention is set to 0.7. Moreover, all the models are trained to use Adam optimizer [Kingma and Ba, 2014] with a learning rate of 0.002 and mini-batches of size 64 to minimize categorical cross-entropy loss. We employ L2-regularizers with the fully connected layer. Also, the dropout is 0.5. |