1. Presentation and clarity: important details with respect to the proposed models are left out or poorly described (more details below). Otherwise, the paper generally reads fairly well; however, the manuscript would need to be improved if accepted.
2. The evaluation on the word analogy task seems a bit unfair given that the semantic relations are explicitly encoded by the sememes, as the authors themselves point out (more details below).
- General Discussion: 1. The authors stress the importance of accounting for polysemy and learning sense-specific representations. While polysemy is taken into account by calculating sense distributions for words in particular contexts in the learning procedure, the evaluation tasks are entirely context-independent, which means that, ultimately, there is only one vector per word -- or at least this is what is evaluated. Instead, word sense disambiguation and sememe information are used for improving the learning of word representations. This needs to be clarified in the paper.
2. It is not clear how the sememe embeddings are learned and the description of the SSA model seems to assume the pre-existence of sememe embeddings. This is important for understanding the subsequent models. Do the SAC and SAT models require pre-training of sememe embeddings?
3. It is unclear how the proposed models compare to models that only consider different senses but not sememes. Perhaps the MST baseline is an example of such a model? If so, this is not sufficiently described (emphasis is instead put on soft vs. hard word sense disambiguation). The paper would be stronger with the inclusion of more baselines based on related work.
4. A reasonable argument is made that the proposed models are particularly useful for learning representations for low-frequency words (by mapping words to a smaller set of sememes that are shared by sets of words). Unfortunately, no empirical evidence is provided to test the hypothesis. It would have been interesting for the authors to look deeper into this. This aspect also does not seem to explain the improvements much since, e.g., the word similarity data sets contain frequent word pairs.
5. Related to the above point, the improvement gains seem more attributable to the incorporation of sememe information than word sense disambiguation in the learning procedure. As mentioned earlier, the evaluation involves only the use of context-independent word representations. Even if the method allows for learning sememe- and sense-specific representations, they would have to be aggregated to carry out the evaluation task.
6. The example illustrating HowNet (Figure 1) is not entirely clear, especially the modifiers of "computer".
7. It says that the models are trained using their best parameters. How exactly are these determined? It is also unclear how K is set -- is it optimized for each model or is it randomly chosen for each target word observation? Finally, what is the motivation for setting K' to 2?
3. It is unclear how the proposed models compare to models that only consider different senses but not sememes. Perhaps the MST baseline is an example of such a model? If so, this is not sufficiently described (emphasis is instead put on soft vs. hard word sense disambiguation). The paper would be stronger with the inclusion of more baselines based on related work.