Task: Language Identification
Input (Spanish, English): This work address the problem of supervised learning from strongly labeled data with label noise. This is a very practical and relevant problem in applied machine learning.  The authors note that using sampling approaches such as EM isn't effective, too slow and cannot be integrated into end-to-end training. Thus, they propose to simulate the effects of EM by a noisy adaptation layer, effectively a softmax, that is added to the architecture during training, and is omitted at inference time. The proposed algorithm is evaluated on MNIST and shows improvements over existing approaches that deal with noisy labeled data.  A few comments. 1. There is no discussion in the work about the increased complexity of training for the model with two softmaxes.   2. What is the rationale for having consecutive (serialized) softmaxes, instead of having a compound objective with two losses, or a network with parallel losses and two sets of gradients?  3. The proposed architecture with only two hidden layers isn't not representative of larger and deeper models that are practically used, and it is not clear that shown results will scale to bigger networks.   4. Why is the approach only evaluated on MNIST, a dataset that is unrealistically simple.
Output (English): en