We generally use speaker rationality α = 8.0 based on dev set tuning, and we follow<cite> Monroe et al. (2017)</cite> for other hyper-parameters-with embedding size of 100 and LSTM size of 100 in our meaning functions. Also following this prior work, we allow the LSTM to be bidirectional with learning rate of 0.005, batch size 128, and gradient clipping at 5.0. We train listeners for 10, 000 iterations on the color data and 15, 000 iterations on grid data, evaluating dev set accuracy every 500 iterations. We pick the model with the best accuracy from those evaluated at 500 iteration intervals.