论文标题
检查神经语言模型的修辞能力
Examining the rhetorical capacities of neural language models
论文作者
论文摘要
最近，神经语言模型（LMS）在产生高质量的话语方面表现出了令人印象深刻的能力。尽管许多最近的论文已经分析了LMS中编码的句法方面，但迄今为止尚未分析句子间，修辞知识。在本文中，我们提出了一种定量评估神经LMS的修辞能力的方法。我们通过评估其能力编码一组源自修辞学结构理论（RST）的语言特征，来研究神经LMS的能力来理解话语的修辞。我们的实验表明，基于BERT的LMS的表现优于其他变压器LM，揭示了其中间层表示中更丰富的话语知识。此外，GPT-2和XLNET显然编码了较少的修辞知识，我们建议从语言哲学中进行解释。我们的方法显示了量化神经LMS的修辞能力的途径。
Recently, neural language models (LMs) have demonstrated impressive abilities in generating high-quality discourse. While many recent papers have analyzed the syntactic aspects encoded in LMs, there has been no analysis to date of the inter-sentential, rhetorical knowledge. In this paper, we propose a method that quantitatively evaluates the rhetorical capacities of neural LMs. We examine the capacities of neural LMs understanding the rhetoric of discourse by evaluating their abilities to encode a set of linguistic features derived from Rhetorical Structure Theory (RST). Our experiments show that BERT-based LMs outperform other Transformer LMs, revealing the richer discourse knowledge in their intermediate layer representations. In addition, GPT-2 and XLNet apparently encode less rhetorical knowledge, and we suggest an explanation drawing from linguistic philosophy. Our method shows an avenue towards quantifying the rhetorical capacities of neural LMs.
