1). Technically speaking, the contribution of this work is incremental, and its technique deep is shallow. The proposed probabilistic word dropout is not that impressive or novel. To me, it sounds like a probabilistic teacher forcing. The massive notations and formulas appear to be not that necessary for me to understand the idea. 2). The improvements on three tasks over the previous works and self-implemented baselines are marginal. Further analysis beyond the main experiments is not sufficient. 3). The backbone is constrained to be the double LSTM, while popular transformers is not involved. Although this seems to work for different encoders, pre-trained language models are not covered as well. The application of this method to more extensive model structures remains a potential concern.
Limitations are mentioned in Section 6, while it seems to be not sufficient.
2). The improvements on three tasks over the previous works and self-implemented baselines are marginal. Further analysis beyond the main experiments is not sufficient.