Task: Language Identification
Input (Spanish, English): - Strengths:  This paper presents a sophisticated application of Grid-type Recurrent Neural Nets to the task of determining predicate-argument structures (PAS) in Japanese.  The approach does not use any explicit syntactic structure, and outperforms the current SOA systems that do include syntactic structure.  The authors give a clear and detailed description of the implementation and of the results.  In particular, they pay close attention to the performance on dropped arguments, zero pronouns, which are prevalent in Japanese and especially challenging with respect to PAS. Their multi-sequence model, which takes all of the predicates in the sentence into account, achieves the best performance for these examples.  The paper is detailed and clearly written.  - Weaknesses:  I really only have minor comments. There are some typos listed below, the correction of which would improve English fluency. I think it would be worth illustrating the point about the PRED including context around the "predicate" with the example from Fig 6 where the accusative marker is included with the verb in the PRED string.  I didn't understand the use of boldface in Table 2, p. 7.  - General Discussion:  Typos:  p1 :  error propagation does not need a "the", nor does "multi-predicate interactions" p2: As an solution -> As a solution, single-sequence model -> a single-sequence model,                    multi-sequence model -> a multi-sequence model  p. 3 Example in Fig 4.                    She ate a bread -> She ate bread. p. 4 assumes the independence -> assumed independence, the multi-predicate interactions -> multi-predicate interactions, the multi-sequence model -> a multi-sequence model p.7: the residual connections -> residual connections, the multi-predicate interactions -> multi-predicate interactions (twice) p8 NAIST Text Corpus -> the NAIST Text Corpus, the state-of-the-art result -> state-of-the-art results  I have read the author response and am satisfied with it.
Output (English): en