Task: Language Identification
Input (Spanish, English): The authors propose a RNN-method for time-series classification with missing values, that can make use of potential information in missing values. It is based on a simple linear imputation of missing values with learnable parameters. Furthermore, time-intervals between missing values are computed and used to scale the RNN computation downstream. The authors demonstrate that their method outperforms reasonable baselines on (small to mid-sized) real world datasets. The paper is clearly written. IMO the authors propose a reasonable approach for dealing with missing values for their intended application domain, where data is not abundant and requires smallish models. I’m somewhat sceptical if the benefits would carry over to big datasets, where more general, less handcrafted multi-layer RNNs are an option.
Output (English): en