Task: Language Identification
Input (Spanish, English): - Strengths: The paper proposes an end-to-end neural model for semantic graph parsing, based on a well-designed transition system.  The work is interesting, learning semantic representations of DMRS, which is capable of resolving semantics such as scope underspecification. This work shows a new scheme for computational semantics, benefiting from an end-to-end transition-based incremental framework, which resolves the parsing with low cost.  - Weaknesses:   My major concern is that the paper only gives a very common introduction for the definition of DMRS and EP, and the example even makes me a little confused because I cannot see anything special for DMRS. The description can be a little more detailed, I think. However, upon the space limitation, it is understandable. The same problem exists for the transition system of the parsing model. If I do not have any background of MRS and EP, I can hardly learn something from the paper, just seeing that this paper is very good.  - General Discussion:   Overall, this paper is very interesting to me. I like the DMRS for semantic parsing very much and like the paper very much. Hope that the open-source codes and datasets can make this line of research being a hot topic.
Output (English): en