Arguments facilitate humans to deliver their ideas. The outcome of the discussion heavily relies on the validity of the argument. If an argument is well-composed, it is more effective to grasp the core idea behind the argument. To grade the argument, machines can be utilized by decomposing into semantic label components. In natural language processing, multiple language models are available to perform this task. It is divided into context-free and contextual models. The majority of previous studies used hand-crafted features to perform argument component classification, while state of the art language models utilize machine learning. The majority of these language models ignore the context in an argument. This research paper aims to analyze whether by including the context in the classification process may improve the accuracy of the language model which will enhance the argumentation mining process as well. The same document corpus is fed into several language models. Word2Vec and GLoVe represent the context free models, while BERT and ELMo as context sensitive language models. Accuracy and time from each model are then compared to determine the importance of context. The result shows that contextual language models are proven to be able to boost classification accuracy by approximately 20%. However, time comes as a cost where contextual models require longer training and prediction time. The benefit from the increase in accuracy outweighs the burden of time. Thus, as a contextual task, argumentation mining is suggested to use contextual model where context must be included to achieve promising results.

论证有助于人类表达他们的想法。讨论的结果在很大程度上取决于论证的有效性。如果一个论点结构合理，那么掌握论点背后的核心思想会更有效。为了对论证进行评分，可以通过分解为语义标签组件来利用机器。在自然语言处理中，可以使用多种语言模型来执行此任务。它分为上下文无关模型和上下文模型。以前的大多数研究使用手工制作的特征来执行参数组件分类，而最先进的语言模型利用机器学习。这些语言模型中的大多数都忽略了参数中的上下文。本研究论文旨在分析在分类过程中加入上下文是否可以提高语言模型的准确性，这也将增强论证挖掘过程。相同的文档语料库被输入到多个语言模型中。Word2Vec 和 GLoVe 表示上下文无关模型，而 BERT 和 ELMo 表示上下文敏感语言模型。然后比较每个模型的准确性和时间以确定上下文的重要性。结果表明，上下文语言模型被证明能够将分类准确率提高约 20%。然而，在上下文模型需要更长的训练和预测时间的情况下，时间是一种成本。准确性提高带来的好处超过了时间的负担。因此，作为上下文任务，