在序列标记解析中，并非所有线性化都同样需要数据
Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing
摘要：
已经提出了不同的线性化来将依存句法分析转换为序列标记，并将任务解决为：（i）头部选择问题，（ii）找到标记弧作为括号字符串的表示，或（iii）将基于转换的解析器的部分转换序列与单词相关联。然而，人们对这些线性化在低资源设置中的行为知之甚少。在这里，我们首先研究它们的数据效率，从一组不同的丰富资源树库中模拟数据受限的设置。其次，我们测试这种差异是否体现在真正的低资源设置。结果表明，头部选择编码的数据效率更高，在理想的（黄金）框架中表现更好，但当运行设置类似于现实世界的低资源配置时，这种优势大大消失，有利于括号格式。
英文摘要：
Different linearizations have been proposed to cast dependency parsing as sequence labeling and solve the task as: (i) a head selection problem, (ii) finding a representation of the token arcs as bracket strings, or (iii) associating partial transition sequences of a transition-based parser to words. Yet, there is little understanding about how these linearizations behave in low-resource setups. Here, we first study their data efficiency, simulating data-restricted setups from a diverse set of rich-resource treebanks. Second, we test whether such differences manifest in truly low-resource setups. The results show that head selection encodings are more data-efficient and perform better in an ideal (gold) framework, but that such advantage greatly vanishes in favour of bracketing formats when the running setup resembles a real-world low-resource configuration.
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。
