Venue: ICLR 2017 conference

Reviews:

final_score: rating: null
review_content: I appreciated the synthetic data experiments as a clear example showing the capabilities of GMUs.  However, I would have liked to see more detail.  You say the (non-GMU) "logistic classifier was not" able to solve the task.  I would have expected it to do OK, but with less reliability due to the noise.  I would also like more intuition about how the gate learned to distinguish the relevant input versus the noise.

I was very happy to see Table 4 with example outputs, but since the main technical contribution of the paper is the gated units, it would be nice to see the gating parameters projected back into text and image space.  What features of each modality indicate it is amore reliable indicator?

Multiplicative operations in deep learning can sometimes be hard to train.  Could you comment on your experiences here?

Could you say more about related work in multiplicative operations in deep learning?

You give some statistics on the text data for the movies.  Can you say something about the image data?  Perhaps even just the size in pixels?

review_title: Questions

final_score: rating: 4: Ok but not good enough - rejection
review_content: The paper introduces Gated Multimodal Units GMUs, which use multiplicative weights to select the degree to which a hidden unit will consider different modalities in determining its activation.  The paper also introduces a new dataset, "Multimodal IMDb," consisting of over 25k movie summaries, with their posters, and labeled genres.

GMUs are related to "mixture of experts" in that different examples will be classified by different parts of the model, (but rather than routing/gating entire examples, individual hidden units are gated separately).  They are related to attention models in that different parts of the input are weighted differently; there the emphasis is on gating modalities of input.

The dataset is a very nice contribution, and there are many experiments varying text representation and single-modality vs two-modality.  What the paper is lacking is a careful discussion, experimentation and analysis in comparison to other multiplicative gate models---which is the core intellectual contribution of the paper.  For example, I could imagine that a mixture of experts or attention models or other gated models might perform very well, and at the very least provide interesting scientific comparative analysis.  I encourage the authors to continue the work, and submit a revised paper when ready.

As is, I consider the paper to be a good workshop paper, but not ready for a major conference.
review_title: Promising work, but too preliminary for a major conference

final_score: rating: 7: Good paper, accept
review_content: Paper proposes Gated Muiltimodal Unit, a building block for connectionist models capable of handling multiple modalities.

(Figure 2) The bimodal case returns weighted activation by gains of gating units, do you do anything special to keep multi-modal case weighted as well? I.e. how the equation for h in section 3.1 would look like for multi-modal case. Also what’s the rationale for using tanh nonlinearity (over, say RELU), is it somehow experimentally optimised choice?

I would find interesting a discussion on a possibility of handling missing data in case one or more modalities are unavailable at test time. Is this possible in the current model to back-off to fewer modalities? Synthetic example may suggest that’s in fact possible. Those numbers, perhaps, could be added to table 2.

In the synthetic experiment, you should compare MGU with the fully-connected MLP model really, with similar complexity - that is - at least two hidden units (as GMU has two such for each modality) followed by logistic regression. At least in terms of capability of drawing decision boundary, those should be comparable.

I think, broader discussion shall be written on the related work associated with mixture of experts models (which is fact are very similar conceptually) as well as multiplicative RNN models [1]. Also, gating unit in LSTM can, in principle, play very similar role when multiple modalities are spliced in the input.

Overall, the paper is interesting, so is the associated (and to be released) dataset.

Minor comments/typos:

Sec. 3.3:  layers and a MLP (see Section 3.4) -> layers and an MLP

Apologies for unacceptably late review.

[1] Multiplicative LSTM for sequence modelling B Krause, L Lu, I Murray, S Renals

review_title: review

final_score: rating: null
review_content: Thanks for the interesting paper. I have two questions on the experiments and implementation:

1) As for the baseline of the concatenation method,  the paper implemented by retraining the network. What about first retraining separately and then concatenating them and fine tuning the last layer? That may reduce the overfitting and get better results. 

2) Could illustrate the effects of GMU using some toy data? I think information like decision boundaries and optimization curves will be useful for the users to understand this new model.
review_title: several questions on experiments

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper proposed The Gated Multimodal Unit (GMU) model for information fusion. The GMU learns to decide how modalities influence the activation of the unit using multiplicative gates. The paper collected a large genre dataset from IMDB and showed that GMU gets good performance.

The proposed approach seems quite interesting, and the audience may expect it can be used in general scenarios beyond movie genre prediction. So it is quite straightforward that the paper should test the algorithm in other applications, which was not done yet. That is the biggest shortcoming of this paper in my opinions.  

Another concern lies in how to evaluate the performance of information fusion. The abstract claims "The model improves the macro f-score performance of single-modality models by 30% and 4% with respect to visual and textual information respectively", however, such an improvement is off the key. If two modals are complementary to each other, the fusion results will always be higher. The key fact is how much better than baselines the proposed GMU is. There is a long list of techniques for fusions, so it is difficult to conduct an impressive comparison on only one real dataset. I think GMU did a nice work on movie dataset, but I would also expect other techniques, including fine-tuning, dropout, distillation may help too.  It would be nice if the author could compare these techniques. 

I also hope this paper could talk in more details the connection with mixture-of-expert (MoE) model. Both models are based on the nonlinear gated functions, while both method may suffer from local minimum for optimization on small datasets. I would like more in-depth discussion in their similarity and difference.

To gain more attention for GMU, I would encourage the author to open-source their code and try more datasets.
review_title: GMU is an interesting model, but still it is not straighforward to understand how much better than baselines

Meta-review: The authors propose a Gated Muiltimodal Unit to combine multi-modal information (visual and textual). They also collect a large dataset of movie summers and posters. Overall, the reviewers were quite positive, while AR4 points to related models and feels that the contribution in the current version is too weak for ICLR. The AC read the paper and the authors responses but tends to agree with AR4. The authors are encouraged to strengthen their work and resubmit to a future conference.
Decision: Invite to Workshop Track