Task: Language Identification
Input (Spanish, English): Two things I really liked about this paper: 1. The whole idea of having a data-dependent proposal distribution for MCMC. I wasn't familiar with this, although it apparently was previously published. I went back: the (Zhu, 2000) paper was unreadable. The (Jampani, 2014) paper on informed sampling was good. So, perhaps this isn't a good reason for accepting to ICLR.  2. The results are quite impressive. The rough rule-of-thumb is that optimization can help you speed up code by 10%. The standard MCMC results presented on the paper on randomly-generated programs roughly matches this (15%). The fact that the proposed algorithm get ~33% speedup is quite surprising, and worth publishing.  The argument against accepting this paper is that it doesn't match the goals of ICLR. I don't go to ICLR to hear about generic machine learning papers (we have NIPS and ICML for that). Instead, I go to learn about how to automatically represent data and models. Now, maybe this paper talks about how to represent (generated) programs, so it tangentially lives under the umbrella of ICLR. But it will compete against more relevant papers in the conference -- it may just be a poster. Sending this to a programming language conference may have more eventual impact.  Nonetheless, I give this paper an "accept", because I learned something valuable and the results are very good.
Output (English): en