关于gSCAN的系统推广：什么是几乎解决的，下一步是什么？
Systematic Generalization on gSCAN: What is Nearly Solved and What is Next?
摘要：
我们分析了接地扫描（gSCAN）基准，这是最近提出的研究系统的概括接地语言理解。首先，我们研究了原始基准的哪些方面可以通过多模态研究中常用的方法来解决。我们发现，具有跨模态注意力的通用基于transformer的模型在大多数gSCAN分割上都实现了强大的性能，令人惊讶地优于先前工作中更专业的方法。此外，我们的分析表明，许多剩余的错误揭示了同样的根本挑战，在系统的语言结构的概括，无论视觉环境。其次，受这一发现的启发，我们提出了具有挑战性的新任务gSCAN生成数据，将对象之间的关系，在视觉环境中。最后，我们发现，目前的模型是令人惊讶的数据效率低，在gSCAN的命令范围狭窄，这表明未来的工作的另一个挑战。
英文摘要：
We analyze the grounded SCAN (gSCAN) benchmark, which was recently proposed to study systematic generalization for grounded language understanding. First, we study which aspects of the original benchmark can be solved by commonly used methods in multi-modal research. We find that a general-purpose Transformer-based model with cross-modal attention achieves strong performance on a majority of the gSCAN splits, surprisingly outperforming more specialized approaches from prior work. Furthermore, our analysis suggests that many of the remaining errors reveal the same fundamental challenge in systematic generalization of linguistic constructs regardless of visual context. Second, inspired by this finding, we propose challenging new tasks for gSCAN by generating data to incorporate relations between objects in the visual environment. Finally, we find that current models are surprisingly data inefficient given the narrow scope of commands in gSCAN, suggesting another challenge for future work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
