概率分布坍缩：紧凑无监督神经文法归纳的关键瓶颈
Probability Distribution Collapse: A Critical Bottleneck to Compact Unsupervised Neural Grammar Induction
AI总结：
本文针对无监督神经文法归纳中的概率分布坍缩问题，提出坍缩松弛神经参数化方法，在提升句法分析性能的同时实现更紧凑的文法表示。
摘要：
无监督神经文法归纳旨在从语言数据中学习可解释的层次结构。然而，现有模型面临表达能力瓶颈，常常导致文法规模不必要地庞大但性能欠佳。我们识别出一个核心问题——概率分布坍缩——作为这一局限的根本原因。我们分析了坍缩在神经参数化的关键组件中何时以及如何出现，并引入了一种针对性的解决方案——坍缩松弛神经参数化——以缓解该问题。我们的方法显著提升了句法分析性能，同时使得在广泛语言中能够使用明显更紧凑的文法，这一点通过大量实证分析得到了证明。
英文摘要：
Unsupervised neural grammar induction aims to learn interpretable hierarchical structures from language data. However, existing models face an expressiveness bottleneck, often resulting in unnecessarily large yet underperforming grammars. We identify a core issue, $\textit{probability distribution collapse}$, as the underlying cause of this limitation. We analyze when and how the collapse emerges across key components of neural parameterization and introduce a targeted solution, $\textit{collapse-relaxing neural parameterization}$, to mitigate it. Our approach substantially improves parsing performance while enabling the use of significantly more compact grammars across a wide range of languages, as demonstrated through extensive empirical analysis.
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。
