论文标题
转移词汇语义家族的辩论性话语单位身份的学习
Transfer Learning of Lexical Semantic Families for Argumentative Discourse Units Identification
论文作者
论文摘要
论证挖掘任务需要知情的低到高复杂性语言现象和常识性知识的知情范围。先前的工作表明，预训练的语言模型在使用转移学习技术应用并建立在不同的训练前目标上时，在编码语法和语义语言现象方面非常有效。它仍然是一个问题，即现有的预训练的语言模型涵盖了参数挖掘任务的复杂性。我们依靠实验来阐明从不同词汇语义家族获得的语言模型如何利用识别争论性话语单位任务的绩效。实验结果表明，转移学习技术对任务有益，并且当前的方法可能不足以利用来自不同词汇语义家族的常识性知识。
Argument mining tasks require an informed range of low to high complexity linguistic phenomena and commonsense knowledge. Previous work has shown that pre-trained language models are highly effective at encoding syntactic and semantic linguistic phenomena when applied with transfer learning techniques and built on different pre-training objectives. It remains an issue of how much the existing pre-trained language models encompass the complexity of argument mining tasks. We rely on experimentation to shed light on how language models obtained from different lexical semantic families leverage the performance of the identification of argumentative discourse units task. Experimental results show that transfer learning techniques are beneficial to the task and that current methods may be insufficient to leverage commonsense knowledge from different lexical semantic families.
