论文标题
组成概括需要组成解析器
Compositional Generalization Requires Compositional Parsers
论文作者
论文摘要
一项关于组成概括的研究迅速发展，研究了语义解析器在训练中训练中观察到的序列中动态重组语言元素的能力。我们介绍了序列与序列模型和模型的系统比较，这些模型由最近的COGS语料库（Kim and Linzen，2020）引导的组成原理。尽管SEQ2SEQ模型可以在词汇任务上表现良好，但它们在需要新的句法结构的结构概括任务上以接近零的精度执行。即使经过训练来预测语法而不是语义，这也是如此。相反，组成模型在结构概括方面具有接近完美的精度。我们提出了新的结果，从AM解析器中证实了这一点（Groschwitz等，2021）。我们的发现表明结构概括是组成概括的关键度量，需要了解复杂结构的模型。
A rapidly growing body of research on compositional generalization investigates the ability of a semantic parser to dynamically recombine linguistic elements seen in training into unseen sequences. We present a systematic comparison of sequence-to-sequence models and models guided by compositional principles on the recent COGS corpus (Kim and Linzen, 2020). Though seq2seq models can perform well on lexical tasks, they perform with near-zero accuracy on structural generalization tasks that require novel syntactic structures; this holds true even when they are trained to predict syntax instead of semantics. In contrast, compositional models achieve near-perfect accuracy on structural generalization; we present new results confirming this from the AM parser (Groschwitz et al., 2021). Our findings show structural generalization is a key measure of compositional generalization and requires models that are aware of complex structure.
