Metric: Per-class Accuracy (Disagree) (higher is better)
| # | Model↕ | Per-class Accuracy (Disagree)▼ | Extra Data | Paper | Date↕ | Code |
|---|---|---|---|---|---|---|
| 1 | ZAINAB A. JAWAD, AHMED J. OBAID (CNN and DNN with SCM, 2022) | 96 | No | Combination Of Convolution Neural Networks And D... | 2022-10-15 | - |
| 2 | Sepúlveda-Torres R., Vicente M., Saquete E., Lloret E., Palomar M. (2021) | 63.41 | No | - | - | Code |
| 3 | Bi-LSTM (max-pooling, attention) | 10.33 | No | Combining Similarity Features and Deep Represent... | 2018-11-02 | Code |
| 4 | Baseline based on word2vec + hand-crafted features (Bhatt et al., 2017) | 9.61 | No | On the Benefit of Combining Neural, Statistical ... | 2017-12-11 | Code |
| 5 | 3rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017) | 6.6 | No | A simple but tough-to-beat baseline for the Fake... | 2017-07-11 | Code |
| 6 | Bhatt et al. | 6.31 | No | On the Benefit of Combining Neural, Statistical ... | 2017-12-11 | Code |
| 7 | Neural baseline based on bi-directional LSTMs (Bhatt et al., 2017) | 4.59 | Yes | On the Benefit of Combining Neural, Statistical ... | 2017-12-11 | Code |
| 8 | Baseline based on skip-thought embeddings (Bhatt et al., 2017) | 0 | No | - | - | Code |