论文标题
关于预训练的语言模型在单词排序中的作用：与Bart的案例研究
On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART
论文作者
论文摘要
单词订购是一个受约束的语言生成任务，以无序的单词为输入。现有工作使用线性模型和神经网络来完成任务，但尚未在单词顺序中研究预训练的语言模型，更不用说它们为何提供帮助了。我们将BART用作实例，并在任务中显示其有效性。为了解释为什么BART有助于单词排序，我们通过探测扩展分析，并从经验上确定BART中的句法依赖性知识是可靠的解释。我们还报告了相关的部分树线性化任务中BART的性能提高，这很容易扩展我们的分析。
Word ordering is a constrained language generation task taking unordered words as input. Existing work uses linear models and neural networks for the task, yet pre-trained language models have not been studied in word ordering, let alone why they help. We use BART as an instance and show its effectiveness in the task. To explain why BART helps word ordering, we extend analysis with probing and empirically identify that syntactic dependency knowledge in BART is a reliable explanation. We also report performance gains with BART in the related partial tree linearization task, which readily extends our analysis.
