论文标题
评估变压器到抽象句法表示的能力：基于长距离协议的对比分析
Assessing the Capacity of Transformer to Abstract Syntactic Representations: A Contrastive Analysis Based on Long-distance Agreement
论文作者
论文摘要
长距离协议（句法结构的证据）越来越多地用于评估神经语言模型的句法概括。许多工作表明，变压器能够在各种协议任务中具有很高的准确性，但是模型实现此行为的机制仍未得到充分了解。为了更好地理解变形金刚的内部工作，这项工作与他们如何处理两个表面上相似但理论上独特的一致性现象的方式与法语中的主题 - 驱动和对象 - 词法一致。使用探测和反事实分析方法，我们的实验表明，i）一致性任务遭受了几个混杂因素，这些混杂因素部分质疑到目前为止得出的结论，ii）变形金刚以与他们在理论语言学中的建模相一致的方式处理主题 - 词语和对象 - 分词协议。
The long-distance agreement, evidence for syntactic structure, is increasingly used to assess the syntactic generalization of Neural Language Models. Much work has shown that transformers are capable of high accuracy in varied agreement tasks, but the mechanisms by which the models accomplish this behavior are still not well understood. To better understand transformers' internal working, this work contrasts how they handle two superficially similar but theoretically distinct agreement phenomena: subject-verb and object-past participle agreement in French. Using probing and counterfactual analysis methods, our experiments show that i) the agreement task suffers from several confounders which partially question the conclusions drawn so far and ii) transformers handle subject-verb and object-past participle agreements in a way that is consistent with their modeling in theoretical linguistics.
