The ability to compositionally map language to referents, relations, and
actions is an essential component of language understanding. The recent gSCAN
dataset (Ruis et al. 2020, NeurIPS) is an inspiring attempt to assess the
capacity of models to learn this kind of grounding in scenarios involving
navigational instructions. However, we show that gSCAN's highly constrained
design means that it does not require compositional interpretation and that
many details of its instructions and scenarios are not required for task
success. To address these limitations, we propose ReaSCAN, a benchmark dataset
that builds off gSCAN but requires compositional language interpretation and
reasoning about entities and relations. We assess two models on ReaSCAN: a
multi-modal baseline and a state-of-the-art graph convolutional neural model.
These experiments show that ReaSCAN is substantially harder than gSCAN for both
neural architectures. This suggests that ReaSCAN can serve as a valuable
benchmark for advancing our understanding of models' compositional
generalization and reasoning capabilities.

本文介绍了一个新的基准数据集 ReaSCAN，它比现有的语言指导场景 gSCAN 更具挑战性，可以用于评估模型的复合泛化和推理能力。

ReaSCAN: 语言基础中的组合推理

ReaSCAN: Compositional Reasoning in Language Grounding

Humans are remarkably flexible when understanding new sentences that include
combinations of concepts they have never encountered before. Recent work has
shown that while deep networks can mimic some human language abilities when
presented with novel sentences, systematic variation uncovers the limitations
in the language-understanding abilities of networks. We demonstrate that these
limitations can be overcome by addressing the generalization challenges in the
gSCAN dataset, which explicitly measures how well an agent is able to interpret
novel linguistic commands grounded in vision, e.g., novel pairings of
adjectives and nouns. The key principle we employ is compositionality: that the
compositional structure of networks should reflect the compositional structure
of the problem domain they address, while allowing other parameters to be
learned end-to-end. We build a general-purpose mechanism that enables agents to
generalize their language understanding to compositional domains. Crucially,
our network has the same state-of-the-art performance as prior work while
generalizing its knowledge when prior work does not. Our network also provides
a level of interpretability that enables users to inspect what each part of
networks learns. Robust grounded language understanding without dramatic
failures and without corner cases is critical to building safe and fair robots;
we demonstrate the significant role that compositionality can play in achieving
that goal.

提出通过遵循组成性原则来训练神经网络解决语言理解的挑战，并通用地应用于多种领域，进而提高机器人的安全性和公平性，所提出的网络能够显着提高自然语言理解能力，且在实现先前工作所不能实现的泛化能力的同时还提供了可解释性的层面。