Task: Language Identification
Input (Spanish, English): 1) Summary  This paper proposes an end-to-end hybrid architecture to predict the local linear trends of time series. A temporal convnet on raw data extracts short-term features. In parallel, long term representations are learned via a LSTM on piecewise linear approximations of the time series. Both representations are combined using a MLP with one hidden layer (in two parts, one for each stream), and the entire architecture is trained end-to-end by minimizing (using Adam) the (l2-regularized) euclidean loss w.r.t. ground truth local trend durations and slopes.   2) Contributions  + Interesting end-to-end architecture decoupling short-term and long-term representation learning in two separate streams in the first part of the architecture. + Comparison to deep and shallow baselines.  3) Suggestions for improvement  Add a LRCN baseline and discussion: The benefits of decoupling short-term and long-term representation learning need to be assessed by comparing to the popular "long-term recurrent convolutional network" (LRCN) of Donahue et al (
Output (English): en