- Update after rebuttal  I appreciate the authors taking the time to clarify their implementation of the baseline and to provide some evidence of the significance of the improvements they report. These clarifications should definitely be included in the camera-ready version. I very much like the idea of using visual features for these languages, and I am looking forward to seeing how they help more difficult tasks in future work.  - Strengths:  - Thinking about Chinese/Japanese/Korean characters visually is a great idea!  - Weaknesses:  - Experimental results show only incremental improvement over baseline, and the choice of evaluation makes it hard to verify one of the central arguments: that visual features improve performance when processing rare/unseen words.  - Some details about the baseline are missing, which makes it difficult to interpret the results, and would make it hard to reproduce the work.  - General Discussion:  The paper proposes the use of computer vision techniques (CNNs applied to images of text) to improve language processing for Chinese, Japanese, and Korean, languages in which characters themselves might be compositional. The authors evaluate their model on a simple text-classification task (assigning Wikipedia page titles to categories). They show that a simple one-hot representation of the characters outperforms the CNN-based representations, but that the combination of the visual representations with standard one-hot encodings performs better than the visual or the one-hot alone. They also present some evidence that the visual features outperform the one-hot encoding on rare words, and present some intuitive qualitative results suggesting the CNN learns good semantic embeddings of the characters.  I think the idea of processing languages like Chinese and Japanese visually is a great one, and the motivation for this paper makes a lot of sense. However, I am not entirely convinced by the experimental results. The evaluations are quite weak, and it is hard to say whether these results are robust or simply coincidental. I would prefer to see some more rigorous evaluation to make the paper publication-ready. If the results are statistically significant (if the authors can indicate this in the author response), I would support accepting the paper, but ideally, I would prefer to see a different evaluation entirely.  More specific comments below:  - In Section 3, paragraph "lookup model", you never explicitly say which embeddings you use, or whether they are tuned via backprop the way the visual embeddings are. You should be more clear about how the baseline was implemented. If the baseline was not tuned in a task-specific way, but the visual embeddings were, this is even more concerning since it makes the performances substantially less comparable.  - I don't entirely understand why you chose to evaluate on classifying wikipedia page titles. It seems that the only real argument for using the visual model is its ability to generalize to rare/unseen characters. Why not focus on this task directly? E.g. what about evaluating on machine translation of OOV words? I agree with you that some languages should be conceptualized visually, and sub-character composition is important, but the evaluation you use does not highlight weaknesses of the standard approach, and so it does not make a good case for why we need the visual features.   - In Table 5, are these improvements statistically significant?  - It might be my fault, but I found Figure 4 very difficult to understand. Since this is one of your main results, you probably want to present it more clearly, so that the contribution of your model is very obvious. As I understand it, "rank" on the x axis is a measure of how rare the word is (I think log frequency?), with the rarest word furthest to the left? And since the visual model intersects the x axis to the left of the lookup model, this means the visual model was "better" at ranking rare words? Why don't both models intersect at the same point on the x axis, aren't they being evaluated on the same set of titles and trained with the same data? In the author response, it would be helpful if you could summarize the information this figure is supposed to show, in a more concise way.   - On the fallback fusion, why not show performance for for different thresholds? 0 seems to be an edge-case threshold that might not be representative of the technique more generally.  - The simple/traditional experiment for unseen characters is a nice idea, but is presented as an afterthought. I would have liked to see more eval in this direction, i.e. on classifying unseen words  - Maybe add translations to Figure 6, for people who do not speak Chinese?
Output: en