依赖树中标点符号的生成模型
A Generative Model for Punctuation in Dependency Trees
摘要：
树库传统上将标点符号视为普通单词，但语言学家认为树的“真正”标点符号不会被观察到（Nunberg，1990）。这些潜在的“底层”标记用于在语法树中划分或分离成分。当树的yield被呈现为书面句子时，字符串重写机制将底层标记转换为“表面”标记，这些标记是观察到的（表面）字符串的一部分，但不应被视为树的一部分。我们正式承认有效的动态规划标点符号的生成模型，这个想法。我们通过局部最大化不完整数据的可能性（类似于EM），在不观察底层标记的情况下对其进行训练。当我们使用经过训练的模型来重建树的底层标点符号时，结果在5种语言中似乎是合理的，特别是与Nunberg对英语的分析一致。我们表明，我们的生成模型可以用来击败标点符号恢复的基线。此外，我们对句子底层标点符号的重建可以让我们在句法转换句子时适当地呈现表层标点符号（通过我们训练的底层到表层机制）。
英文摘要：
Treebanks traditionally treat punctuation marks as ordinary words, but linguists have suggested that a tree's "true" punctuation marks are not observed (Nunberg, 1990). These latent "underlying" marks serve to delimit or separate constituents in the syntax tree. When the tree's yield is rendered as a written sentence, a string rewriting mechanism transduces the underlying marks into "surface" marks, which are part of the observed (surface) string but should not be regarded as part of the tree. We formalize this idea in a generative model of punctuation that admits efficient dynamic programming. We train it without observing the underlying marks, by locally maximizing the incomplete data likelihood (similarly to EM). When we use the trained model to reconstruct the tree's underlying punctuation, the results appear plausible across 5 languages, and in particular, are consistent with Nunberg's analysis of English. We show that our generative model can be used to beat baselines on punctuation restoration. Also, our reconstruction of a sentence's underlying punctuation lets us appropriately render the surface punctuation (via our trained underlying-to-surface mechanism) when we syntactically transform the sentence.
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。
