Scalable Multi-Hop Relational Reasoning forKnowledge-Aware Question Answering
Table 8: Learning rate for graph encoders on different datasets.
['[EMPTY]', 'CommonsenseQA', 'OpenbookQA']
Our models are implemented in PyTorch. We use cross-entropy loss and RAdam (Liu et al., We find it beneficial to use separate learning rates for the text encoder and the graph encoder. We tune learning rates for text encoders and graph encoders on two datasets. We report the performance of these fine-tuned text encoders and also adopt their dataset-specific optimal learning rates in joint training with graph encoders. In training, we set the maximum input sequence length to text encoders to 64, batch size to 32, and perform early stopping.