Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue.Because dialogues have the characteristics of high personal pronoun occurrences and low information density, and since most relational facts in dialogues are not supported by any single sentence, dialogue-based relation extraction requires a comprehensive understanding of dialogue.In this paper, we propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues.In addition, we propose a novel approach which treats the task of emotion recognition in conversations (ERC) as a dialoguebased RE.Experiments on a dialogue-based RE dataset and three ERC datasets demonstrate that our model is very effective in various dialogue-based natural language understanding tasks.In these experiments, TUCORE-GCN outperforms the state-of-theart models on most of the benchmark datasets.Our code is available at https://github. com/BlackNoodle/TUCORE-GCN.