通过迭代打乱研究词序
Studying word order through iterative shuffling
AI总结：
该研究通过提出迭代打乱推理（IBIS）算法，在GLUE套件等场景中反驳了“词序编码NLP任务必需意义”的假设，证明多数情况下词序打乱难改变信息，且IBIS优于现有词序算法，可用于语言建模与约束生成。
摘要：
当神经语言模型在NLP基准任务上接近人类表现时，其进展被广泛视为对句法理解日益复杂的证据。这一观点基于一个尚未经实证检验的假设：词序编码了执行这些任务所必需的意义。我们在许多情况下反驳了这一假设：在GLUE套件和各类英文文本中，句子或短语中的词语很少能通过排列形成承载显著不同信息的短语。我们的意外结果依赖于迭代打乱推理（IBIS），这是一种新颖、高效的过程，可在固定语言模型下找到词袋具有最高似然的排序。IBIS可使用任何黑箱模型而无需额外训练，且优于现有词序算法。结合我们的发现，我们讨论了诸如IBIS之类的打乱推理过程如何有益于语言建模和约束生成。
英文摘要：
As neural language models approach human performance on NLP benchmark tasks, their advances are widely seen as evidence of an increasingly complex understanding of syntax. This view rests upon a hypothesis that has not yet been empirically tested: that word order encodes meaning essential to performing these tasks. We refute this hypothesis in many cases: in the GLUE suite and in various genres of English text, the words in a sentence or phrase can rarely be permuted to form a phrase carrying substantially different information. Our surprising result relies on inference by iterative shuffling (IBIS), a novel, efficient procedure that finds the ordering of a bag of words having the highest likelihood under a fixed language model. IBIS can use any black-box model without additional training and is superior to existing word ordering algorithms. Coalescing our findings, we discuss how shuffling inference procedures such as IBIS can benefit language modeling and constrained generation.
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。
