Gated Multimodal Units for Information Fusion
This paper presents a novel model for multimodal learning based on gated neural networks. The Gated Multimodal Unit (GMU) model is intended to be used as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities. The GMU learns to decide how modalities influence the activation of the unit using multiplicative gates. It was evaluated on a multilabel scenario for genre classification of movies using the plot and the poster. The GMU improved the macro f-score performance of single-modality approaches and outperformed other fusion strategies, including mixture of experts models. Along with this work, the MM-IMDb dataset is released which, to the best of our knowledge, is the largest publicly available multimodal dataset for genre prediction on movies.
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.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Gated Multimodal Units for Information Fusion
This paper presents a novel model for multimodal learning based on gated neural networks. The Gated Multimodal Unit (GMU) model is intended to be used as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities. The GMU learns to decide how modalities influence the activation of the unit using multiplicative gates. It was evaluated on a multilabel scenario for genre classification of movies using the plot and the poster. The GMU improved the macro f-score performance of single-modality approaches and outperformed other fusion strategies, including mixture of experts models. Along with this work, the MM-IMDb dataset is released which, to the best of our knowledge, is the largest publicly available multimodal dataset for genre prediction on movies.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



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.
assistant
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.