Task: Language Identification
Input (Spanish, English): The paper proposes an approach to sequence transduction for the case when a monotonic alignment between the input and the output is plausible. It is assumed that the alignment can be provided as a part of training data, with Chinese Restaurant process being used in the actual experiments.   The idea makes sense, although its applicability is limited to the domains where a monotonic alignment is available. But as discussed during the pre-review period, there has been a lot of strongly overlapping related work, such as probabilistic models with hard-alignment (Sequence Transduction With Recurrent Neural Network, Graves et al, 2012) and also attempts to use external alignments in end-to-end models (A Neural Transducer, Jaitly et al, 2015). That said, I do not think the approach is sufficiently novel.   I also have a concern regarding the evaluation. I do not think it is fair to compare the proposed model that depends on external alignment with the vanilla soft-attention model that learns alignments from scratch. In a control experiment soft-attention could be trained to match the external alignment. Such a pretraining could reduce overfitting on the small dataset, the one on which the proposed approach brings the most improvement. On a larger dataset, especially SIGMORPHON, the improvements are not very big and are only obtained for a certain class of languages.  To sum up, two main issues are (a) lack of novelty (b) the comparison of a model trained with external alignment and one without it.
Output (English): en