长故事上的可微推理--神经模型中的系统概括评估
Differentiable Reasoning over Long Stories -- Assessing Systematic Generalisation in Neural Models
摘要：
当代神经网络在许多方面取得了一系列的发展和成功;然而，当暴露于训练分布之外的数据时，它们可能无法预测正确的答案。在这项工作中，我们关注的是这种泛化问题，因此系统地分析了一系列广泛的模型。相关实验是基于CLUTRR进行的，CLUTRR是一种诊断基准套件，可以通过在小故事图上进行训练并在较大的故事图上进行测试来分析自然语言理解（NLU）系统的泛化。为了处理多关系故事图，我们考虑两类神经模型：“E-GNN”，基于图的模型，可以处理图结构的数据，同时考虑边缘属性;和“L-Graph”，基于序列的模型，可以处理线性版本的图。我们进行了广泛的实证评估，我们发现，修改后的递归神经网络在每一个系统的概括任务中产生令人惊讶的准确结果，优于修改后的图神经网络，而后者产生了更强大的模型。
英文摘要：
Contemporary neural networks have achieved a series of developments and successes in many aspects; however, when exposed to data outside the training distribution, they may fail to predict correct answers. In this work, we were concerned about this generalisation issue and thus analysed a broad set of models systematically and robustly over long stories. Related experiments were conducted based on the CLUTRR, which is a diagnostic benchmark suite that can analyse generalisation of natural language understanding (NLU) systems by training over small story graphs and testing on larger ones. In order to handle the multi-relational story graph, we consider two classes of neural models: "E-GNN", the graph-based models that can process graph-structured data and consider the edge attributes simultaneously; and "L-Graph", the sequence-based models which can process linearized version of the graphs. We performed an extensive empirical evaluation, and we found that the modified recurrent neural network yield surprisingly accurate results across every systematic generalisation tasks which outperform the modified graph neural network, while the latter produced more robust models.
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