Chemical information extraction is to convert chemical knowledge in text into true chemical database, which is a text processing task heavily relying on chemical compound name identification and standardization. Once a systematic name for a chemical compound is given, it will naturally and much simply convert the name into the eventually required molecular formula. However, for many chemical substances, they have been shown in many other names besides their systematic names which poses a great challenge for this task. In this paper, we propose a framework to do the auto standardization from the non-systematic names to the corresponding systematic names by using the spelling error correction, byte pair encoding tokenization and neural sequence to sequence model. Our framework is trained end to end and is fully data-driven. Our standardization accuracy on the test dataset achieves 54.04% which has a great improvement compared to previous state-of-the-art result.
Pros: This seems like very competent and important work in an under-served area: Doing the mapping (or "entity linking") of chemical names to their standardized systematic forms. It's not my area, but I was frankly surprised when the paper said there was only one relevant prior piece of work, but having searched for a few minutes on Google Scholar, I'm at least inclined to believe that the authors are (approximately) right on that one. (This stands in stark contradistinction to the large quantity of biomedical entity recognition and linking work.) So, it's valuable to have work in this area, and the approach and application are sensible. In one sense, this gives the work significance and originality (as to domain). The paper is also clearly written, and certainly sufficiently accessible to an ML reader.
Cons: Unfortunately, though, I just don't think this qualifies for acceptance at ICLR. It's application of known techniques, and lacks any ML novelty or sufficient ML interest. It would only be appropriate for an "ML applications" track, which ICLR does not have. And while its performance is _way_ better than that of the only previous work on the topic that they know, accuracy of mapping non-systematic chemical terms (54.04%) is still low enough that this technique doesn't seem ready for prime time. 
Other comments: In table 5, you show that a prior pipeline stage of spelling correction is definitely useful in your system (table 5). And yet, given the power of deep learning seq2seq transductions, and the potential to use them for spelling correction, one might wonder whether this prior step of spelling correction is really necessary. It might be interesting to explore further where it helps and whether the gains of spelling correction might be obtainable in other ways such as using data augmentation (such as spelling error insertion) in the seq2seq training data. The Golebiewski bib entry is lacking any information as to where it is published, which seems especially bad for the key citation to prior work of the whole paper. In general, the bibliography has issues: non-ASCII characters have been lost (you either need to LaTeX-escape them or to load a package like utf8, and capitalization of acronyms, etc. should be improved with curly braces.