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arXiv 2212.04523cs.CL
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基于长距离一致性的Transformer句法表征能力对比分析

Assessing the Capacity of Transformer to Abstract Syntactic Representations: A Contrastive Analysis Based on Long-distance Agreement

Université Paris Cité(巴黎西岱大学);LLF(语言学形式实验室);CNRS(法国国家科学研究中心)

Bingzhi Li, Guillaume Wisniewski, Benoît Crabbé

摘要:

长距离一致性作为句法结构的证据,越来越多地被用来评估神经语言模型的句法泛化能力。许多工作表明,Transformers能够在不同的协议任务的高精度,但该模型实现这种行为的机制仍然没有得到很好的理解。为了更好地理解Transformers的内部工作,这项工作对比了他们如何处理两个表面上相似但理论上不同的一致现象:法语中的主语-动词和宾语-过去分词一致。使用探测和反事实分析方法,我们的实验表明,i)协议任务遭受了一些混淆,部分质疑迄今为止得出的结论和ii)Transformers处理主语-动词和宾语-过去分词协议的方式是一致的,与他们的建模在理论语言学。

英文摘要:

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.

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。

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