Argument mining consists in the automatic identification of argumentative structures in texts. In this work we leverage existing discourse-level annotations to facilitate the identification of argumentative components and relations in scientific texts, which has been recognized as a particularly challenging task. We propose a new annotation schema and use it to augment a corpus of computational linguistics abstracts that had previously been annotated with discourse units and relations. Our initial experiments with the enriched corpus confirm the potential value of incorporating discourse information in argument mining tasks. In order to tackle the limitations posed by the lack of corpora containing both discourse and argumentative annotations we explore two transfer learning approaches in which discourse parsing is used as an auxiliary task when training argument mining models. In this case, as no discourse information is used as input, the resulting models could be used to predict the argumentative structure of unannotated texts.

论证挖掘在于自动识别文本中的论证结构。在这项工作中，我们利用现有的话语级注释来帮助识别科学文本中的辩论成分和关系，这已被认为是一项特别具有挑战性的任务。我们提出了一种新的注释模式，并使用它来扩充先前已使用话语单元和关系进行注释的计算语言学摘要的语料库。我们对丰富的语料库的初步实验证实了在论点挖掘任务中整合话语信息的潜在价值。为了解决由于缺少包含语篇和论据注释的语料库所带来的局限性，我们探索了两种转移学习方法，其中在训练论证挖掘模型时，将语篇解析用作辅助任务。在这种情况下，由于没有将话语信息用作输入，因此所得模型可用于预测未注释文本的论证结构。