We present a detailed comparison of two types of sequence to sequence models
trained to conduct a compositional task. The models are architecturally
identical at inference time, but differ in the way that they are trained: our
baseline model is trained with a task-success signal only, while the other
model receives additional supervision on its attention mechanism (Attentive
Guidance), which has shown to be an effective method for encouraging more
compositional solutions (Hupkes et al.,2019). We first confirm that the models
with attentive guidance indeed infer more compositional solutions than the
baseline, by training them on the lookup table task presented by Li\v{s}ka et
al. (2019). We then do an in-depth analysis of the structural differences
between the two model types, focusing in particular on the organisation of the
parameter space and the hidden layer activations and find noticeable
differences in both these aspects. Guided networks focus more on the components
of the input rather than the sequence as a whole and develop small functional
groups of neurons with specific purposes that use their gates more selectively.
Results from parameter heat maps, component swapping and graph analysis also
indicate that guided networks exhibit a more modular structure with a small
number of specialized, strongly connected neurons.

本文详细比较了两种序列模型在试图解决合成任务时的差别，通过更多的监督信号，辅助注意机制，参数空间和隐藏层激活机制的不同，发现具有引导性的网络在识别更组合解决方案方面具有更高的效率，并且展现出更加模块化的神经元特点。

神经网络中组合性实现

On the Realization of Compositionality in Neural Networks

While neural network models have been successfully applied to domains that
require substantial generalisation skills, recent studies have implied that
they struggle when solving the task they are trained on requires inferring its
underlying compositional structure. In this paper, we introduce Attentive
Guidance, a mechanism to direct a sequence to sequence model equipped with
attention to find more compositional solutions. We test it on two tasks,
devised precisely to assess the compositional capabilities of neural models,
and we show that vanilla sequence to sequence models with attention overfit the
training distribution, while the guided versions come up with compositional
solutions that fit the training and testing distributions almost equally well.
Moreover, the learned solutions generalise even in cases where the training and
testing distributions strongly diverge. In this way, we demonstrate that
sequence to sequence models are capable of finding compositional solutions
without requiring extra components. These results helps to disentangle the
causes for the lack of systematic compositionality in neural networks, which
can in turn fuel future work.

介绍了 Attentive Guidance，这是一种机制，可以指导序列到序列模型配备关注机制，以找到更多的组合解决方案。通过测试，证明序列到序列模型具备在不需要额外组件的情况下找到组成性解决方案的能力。