We also provide neural baselines that we have run against ARC. The execution framework (also provided) is easily extensible to test new models on the ARC Question Set.
- DecompAttn, based on the Decomposable Attention model of Parikh et al. (2016), a top performer on the SNLI dataset.
- BiDAF, based on the Bidirectional Attention Flow model of Seo et al. (2017), a top performer on the SQuAD dataset.
- DGEM, based on the Decomposable Graph Entailment Model of Khot et al. (2018), a top performer on the SciTail dataset.
- Knowledge-free BiLSTM Max-out model with max-attention from question to choices, adapted by Mihaylov et al. from the model of Conneau et al. (2017).
The models can be downloaded here.