We train our models on the 33k SOCIAL IQA training instances, selecting hyperparameters based on the best performing model on our dev set, for which we then report test results.
we perform finetuning through a grid search over the hyper-parameter settings (with a learning rate in {1e−5, 2e−5, 3e−5}, a batch size in {3, 4, 8}, and a number of epochs in {3, 4, 10}) and report the maximum performance.