Task: Language Identification
Input (Spanish, English): This paper proposes a hybrid architecture that combines traditional CNN layers with separable RNN layers that quickly increase the receptive field of intermediate features. The paper demonstrates experiments on CIfar-10 and semantic segmentation, both by fine-tuning pretrained CNN models and by training them from scratch, showing numerical improvements.     The reviewers agreed that this paper presents a sound modification of standard CNN architectures in a clear, well-presented manner. They also highlighted the clear improvement of the manuscipt between the first draft and subsequent revisions.   However, they also agreed that the novelty of the approach is limited compared to recent works (e.g. Bell'16), despite acknowledging the multiple technical differences between the approaches. Another source of concern is the lack of large-scale experiments on imagenet, which would potentially elucidate the role of the proposed interleaved lrnn modules in the performance boost and demonstrate its usefulness to other tasks.     Based on these remarks, the AC recommends rejection of the current manuscript, and encourages the authors to resubmit the work once the large-scale experiments are completed.
Output (English): en