论文标题
使用预训练的语言模型标记明确的话语关系
Labeling Explicit Discourse Relations using Pre-trained Language Models
论文作者
论文摘要
标记明确的话语关系是浅说话解析的最具挑战性的子任务之一，目的是确定话语连接及其论点的界限。使用手工制作的功能，最先进的模型实现了F-SCORE的45％以上。当前的论文研究了预训练的语言模型在此任务中的功效。我们发现，预先训练的语言模型在经过填充时足够强大，可以取代语言特征。我们在PDTB 2.0上评估了我们的模型，并报告了最先进的结果。这是模型第一次在不采用任何语言特征的情况下优于知识密集型模型。
Labeling explicit discourse relations is one of the most challenging sub-tasks of the shallow discourse parsing where the goal is to identify the discourse connectives and the boundaries of their arguments. The state-of-the-art models achieve slightly above 45% of F-score by using hand-crafted features. The current paper investigates the efficacy of the pre-trained language models in this task. We find that the pre-trained language models, when finetuned, are powerful enough to replace the linguistic features. We evaluate our model on PDTB 2.0 and report the state-of-the-art results in the extraction of the full relation. This is the first time when a model outperforms the knowledge intensive models without employing any linguistic features.
