The language model is pre-trained over speaker utterances paired with targets, but the support of the distribution encoded by this LSTM is too large for the s 1 normalization term within the RSA listener to be computed efficiently. Similar to<cite> Monroe et al. (2017)</cite>, we resolve this issue by taking a small set of samples from the pre-trained LSTM applied to each object in a context, to approximate p(U | O), each time l 1 is computed during training and evaluation.