Task: Language Identification
Input (Spanish, English): - Strengths: This paper introduced a novel method to improve zero pronoun resolution performance.. The main contributions of this papers are: 1) proposed a simple method to automatically generate a large training set for zero pronoun resolution task; 2) adapted a two step learning process to transfer knowledge from large data set to the specific domain data; 3) differentiate unknown words using different tags. In general, the paper is well written. Experiments are thoroughly designed.   - Weaknesses:  But I have a few questions regarding finding the antecedent of a zero pronoun: 1. How will an antecedent be identified, when the prediction is a pronoun? The authors proposed a method by matching the head of noun phrases. It’s not clear how to handle the situation when the head word is not a pronoun. 2. What if the prediction is a noun that could not be found in the previous contents? 3. The system achieves great results on standard data set. I’m curious is it possible to evaluate the system in two steps? The first step is to evaluate the performance of the model prediction, i.e. to recover the dropped zero pronoun into a word; the second step is to evaluate how well the systems works on finding an antecedent.  I’m also curious why the authors decided to use attention-based neural network. A few sentences to provide the reasons would be helpful for other researchers.  A minor comment: In figure 2, should it be s1, s2 … instead of d1, d2 ….?   - General Discussion: Overall it is a great paper with innovative ideas and solid experiment setup.
Output (English): en