Paper:
Visually-grounded embodied language learning models have recently shown to be effective at learning multiple multimodal tasks such as following navigational instructions and answering questions. In this paper, we address two key limitations of these models, (a) the inability to transfer the grounded knowledge across different tasks and (b) the inability to transfer to new words and concepts not seen during training using only a few examples. We propose a multitask model which facilitates knowledge transfer across tasks by disentangling the knowledge of words and visual attributes in the intermediate representations. We create scenarios and datasets to quantify cross-task knowledge transfer and show that the proposed model outperforms a range of baselines in simulated 3D environments. We also show that this disentanglement of representations makes our model modular and interpretable which allows for transfer to instructions containing new concepts. †

Review:
I thank the authors for their detailed response and appreciate their hard work in bringing us this paper. 
I think that my main point is that this work relies too much on the extra information/constraints in the synthetic env. E.g., 1. since the vocab size is small, thus the feature map could be designed 'equal to the vocabulary size' 2. The bag-of-words representation is effective but it is not the case for natural language. Although the authors kindly point me to some recent works on sim2real, I am still not convinced whether this proposed method could be transferred to real setups based on the referenced papers.
However, it is a personal research taste that I always take real setup into considerations, because I have worked on both synthetic and real setup (on both lang and visn sides) for years and observed a large gap. My opinion is that methods of synthetic setups are not naturally convertible to the real ones. If AC/meta-reviewer considers the ability of vision-and-language interactions could be effectively studied through this setup with synthetic language and simulated-unrealistic images, I am OK with acceptance. I have downgraded my confidence scores (but kept my overall score) for this purpose.
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Pros: 
(1) The proposed model makes sense to me, which tries to have two attention layers to extract the information related to the questions. It seems to have the ability to deal with "and"/"or" logical relationships as well. 
(2) Fig. 4 is impressive. It is clear and well-designed. 
(3) The results in Table 2 are convincing. They show that both the proposed dual-attention method and multi-task learning would contribute to the performance. 
Cons:
(1) It seems that the two main contributions are related to the language. Thus the synthetic language might not be proper to study. For example, in Eqn. 2, the first GA multiplies the BOW vector with the vision feature map, which could filter out unrelated instruction. This method could not be directly transferred to a real setup where natural language and natural images are involved.
(2) The designed attention modules is lack of generalizability. It implements a two-step attention module, while the first step selects the related visual regions w.r.t the words and the second step gathers the information regarding these attended regions. However, it might not be aware of the spatial relationships and thus be limited to simple questions. For example, if the question is "What is the object on top of the apple?". To my understanding, the current module would not explicitly handle this one-hop spatial relationship. 
Comments:
(1) According to Sec. 3, 70 instructions and 29 questions are involved in this task. Using GRU to encoder these questions seems to be redundant. A simple one-hot embedding for these instructions might already be enough to encode the information.
(2) I am not sure why the visual attention map x_S could be used as the state of the module.
(3) After Eqn. 3, the paper says that "ReLU activations ... make all elements positive, ensuring ...". I am confused about the intuition behind this argument because of the softmax activation. Softmax will projects 0 to 1. So the sum of the all-zero vector would still be non-zero after softmax. 
Typo:
- In Sec. 4, X_{BoW} \in \{0, 1\}^V.
- In Sec. 4.1, "this matrix is multiplied ..." --> this tensor.