Task: Language Identification
Input (Spanish, English): While the overall direction is promising, there are several serious issues with the paper which affect the novelty and validity of the results:  1. Incorrect claims about related work affecting novelty:    - This work is not the first to explore a deep learning approach to automatic code completion: “Toward Deep Learning Software Repositories”, MSR’15 also uses deep learning for code completion, and is not cited.    - “Code Completion with Statistical Language Models”, PLDI’14 is cited incorrectly -- it also does code completion with recurrent neural networks.    - PHOG is independent of JavaScript -- it does representation learning and has been applied to other languages (e.g., Python, see OOPSLA’16 below).     - This submission is not the only one that “can automatically extract features”. Some high-precision (cited) baselines do it.    - “Structured generative models of natural source code” is an incorrect citation. It is from ICML’14 and has more authors. It is also a log-linear model and conditions on more context than claimed in this submission.   2. Uses a non-comparable prediction task for non-terminal symbols: The type of prediction made here is simpler than the one used in PHOG and state-of-the-art (see OOPSLA’16 paper below) and thus the claimed 11 point improvement is not substantiated. In particular, in JavaScript there are 44 types of nodes. However, a PHOG and OOPSLA’16 predictions considers not only these 44 types, but also whether there are right siblings and children of a node. This is necessary for predicting tree fragments instead of a sequence of nodes. It however makes the prediction harder than the one considered here (it leads to 150+ labels, a >3x increase).   3. Not comparing to state-of-the-art: the state-of-the-art however is not the basic PHOG cited here, but “Probabilistic Model for Code with Decision Trees”, (OOPSLA 2016) which appeared before the submission deadline for ICLR’17:
Output (English): en