Task: Language Identification
Input (Spanish, English): - Strengths:   - The paper is clearly written and well-structured.    - The system newly applied several techniques including global optimization to end-to-end neural relation extraction, and the direct incorporation of the parser representation is interesting.   - The proposed system has achieved the state-of-the-art performance on both ACE05 and CONLL04 data sets.   - The authors include several analyses.  - Weaknesses:   - The approach is incremental and seems like just a combination of existing methods.     - The improvements on the performance (1.2 percent points on dev) are relatively small, and no significance test results are provided.  - General Discussion:  - Major comments:   - The model employed a recent parser and glove word embeddings. How did they affect the relation extraction performance?   - In prediction, how did the authors deal with illegal predictions?  - Minor comments:   - Local optimization is not completely "local". It "considers structural correspondences between incremental decisions," so this explanation in the introduction is misleading.   - Points in Figures 6 and 7 should be connected with straight lines, not curves.   - How are entities represented in "-segment"?   - Some citations are incomplete. Kingma et al. (2014) is accepted to ICLR, and Li et al. (2014) misses pages.
Output (English): en