Paper:
In this paper, we describe a general framework to systematically analyze current neural models for multi-task learning, in which we find that existing models expect to disentangle features into different spaces while features learned in practice are still entangled in shared space, leaving potential hazards for other training or unseen tasks. We propose to alleviate this problem by incorporating a new inductive bias into the process of multi-task learning, that different tasks can communicate with each other not only by passing hidden variables but gradients explicitly. Experimentally, we evaluate proposed methods on three groups of tasks and two types of settings (IN-TASK and OUT-OF-TASK). Quantitative and qualitative results show their effectiveness.
Multi-task learning has been established as an effective learning method in deep learning, driving state-of-theart results to new levels in a number of tasks, ranging from computer vision (Misra et al., 2016;Zamir et al., 2018) to natural language processing (Collobert & Weston, 2008;Luong et al., 2015).
While various kinds of neural multi-task learning approaches have been proposed, a commonality is the two types of feature spaces they defined to store taskspecific features and task-invariant features. And then interaction mechanisms among different feature spaces will be explored in diverse ways, such as hard sharing (Collobert & Weston, 2008) or soft sharing (Misra et al., 2016;Ruder12 et al., 2017) schemes , flat Liu et al., 2016) or structural sharing schemes (Hashimoto et al., 2016;Søgaard & Goldberg, 2016).
Despite their great success, a potential limitation of most existing works is that shared space is entangled with private features since it is not enforced through the use of explicit loss functions (Bousmalis et al., 2016). Specifically, we would have extracted shared features and disentangled them into separated spaces. However, in reality, methods in use just collect all the features together into a common space, instead of learning shared rules across different tasks. This problem, which we define as "pretend-to-share", clearly suffers from the following issues: 1) IN-TASK SETTING: shared space is redundant and contaminated by task-specific features, leaving potential hazards for other training tasks. 2) OUT-OF-TASK SETTING: the learned model can not effectively adapt to new tasks since a real common structure is not discovered.
A few works explore this problem with adversarial training and shows that explicitly disentangling representations into private and shared spaces will improve model's performance (Bousmalis et al., 2016;Liu et al., 2017). In this paper, we propose an orthogonal technique to alleviate above problem. Specifically, we first describe a general framework for multi-task learning called Parameters ReadWrite Networks (PRaWNs) that can simply abstract the commonalities between existing typical neural models for multi-task learning. Then, we explain the reason for pretend-to-share problem within PRaWN that each task has the permission to modify shared parameters without any communication protocol, which makes it easier to update parameters along diverse directions.
Therefore, the problem is translated into this question: how to address the conflict among different tasks happening in the process of parameter updating. We resolve this conflict by introducing an inductive bias for multi-task learning. Specifically, different tasks can communicate with each other by sending (or receiving) both hidden variables and gradients. As shown in Fig.1(b), during the forward phase, tasks A, B, C are capable of sending (and receiving) the gradients to (and from) each other. Then during the backward phase, each task is aware of how other tasks are modifying the parameters, making it possible to modify in a more consistent way. Concretely, by communicating with gradients, we can apply consistency constraint to each task during the process of parameter updating, which can prevent private features from slipping into shared space thereby alleviating the pretend-to-share problem. Technically, we explore two kinds of communication mechanisms for gradient passing: pairwise and list-wise communication, where the latter one can take task relatedness into consideration.
We evaluate our models on three groups of multi-task learning datasets, range from natural language processing tasks to computer vision. The results show that our models are more expressive since we can learn a shared space which are more pure, driving task-dependent features away.
To summarize, we make the following contributions:
• We describe a general framework, which can not only help us analyze current models in an unified way [Section 3], but find potential limitations, and move forward with suitable approaches [Section 4].
• We study a task-agnostic problem existing in many multi-task learning models, and propose to alleviate it by allowing different tasks to communicate with gradients.
• We present an elaborate qualitative analysis, giving some insight into the internal mechanisms of proposed gradient passing method.

Review:
This paper tries to address the"pretend-to-share" problem by designing the gradient passing schemes in which the gradient updates to specific parameters of tasks are passed to the shared parameters. Besides, the authors summarize existing multitask learning algorithms in a framework called Parameters Read-Write Networks (PRAWN). 
Pros:
- The view of putting existing multi-task learning algorithms in a read-write framework is quite intriguing and inspiring.
Cons:
- Motivation: The whole paper is assumed to address the "pretend-to-share" problem, while the authors never provide any evidence that such problem really exists for any other algorithm. It seems to be an assumption without any support.
- Method:  
   - Though the read-write framework is very interesting, the authors do not clearly present it, so that the readers can be totally get lost. For example, what do you mean by writing {\Theta^{*r}_k - \theta^{swr}_k}? In the line of structural read-op, where are \theta_3 and \theta_4  in the column of the constituent para. ? What do you mean by writing the equation (4)? How do you define g() in equation (8)? This is a research paper which should be as clear as possible for the readers to reproduce the results, rather than a proposal only with such abstract and general functions defined. 
   - In the list-wise communication scheme, you define the task relationship in equation (11). The problem is how do you evaluate the effectiveness of such definition, since massive works in multitask learning pursue to learn the task relationship automatically to guarantee the effectiveness instead of such heuristic definition. 
- Related works: The authors do not clearly and correctly illustrate the connections between this work and meta-learning/domain adaptation. To my best knowledge, meta-learning, including MAML (Finn et al. 2017), can obviously solve both in-task setting and out-task setting. In some sense, I think this work is almost equivalent to MAML. 
- Experiments: 
   - First, several state-of-the-art baselines including MAML and cross-stitch networks should be compared. Specifically, for the text classification dataset, there have been a lot of domain adaptation works discovering the transferable pivots (shared features) and non-pivots (specific features), which the authors should be aware of and compare in Table 3.  
   - The Figure 5 is not clear to me, and so is the discussion. The authors try to explain that the updating direction of shared parameters for PGP-SR is an integration of two private updating directions. I tried hard to understand, but still think that Figure 5(a) is even better than Figure 5(b). The updating direction of the shared parameters is almost the same as the cyan line.
- Presentation: there are so many grammatical errors and typos. For example,
   - In the introduction, "...datasets, range from natural" -> "...datasets, ranging from natural"
  - In the related work, "and they propose address it with adversarial" -> "and they propose to address it with adversarial"
 - In the beginning of Section 4, " an general" -> "a general"