Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Evidence-Aware Hierarchical Interactive Attention Networks for Explainable Claim Verification
Authors: Lianwei Wu, Yuan Rao, Xiong Yang, Wanzhen Wang, Ambreen Nazir
IJCAI 2020 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |