Graph Based Network with Contextualized Representations of Turns in Dialogue
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 dialogue-based 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-the-art models on most of the benchmark datasets. Our code is available at https://github.com/BlackNoodle/TUCORE-GCN.
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Datasets
Results from the Paper
Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
---|---|---|---|---|---|---|
Emotion Recognition in Conversation | DailyDialog | TUCORE-GCN_RoBERTa | Micro-F1 | 61.91 | # 3 | |
Emotion Recognition in Conversation | DailyDialog | TUCORE-GCN_BERT | Micro-F1 | 58.34 | # 14 | |
Dialog Relation Extraction | DialogRE | TUCORE-GCN_RoBERTa | F1c (v2) | 65.9 | # 5 | |
F1 (v2) | 73.1 | # 4 | ||||
Dialog Relation Extraction | DialogRE | TUCORE-GCN_BERT | F1c (v2) | 60.2 | # 9 | |
F1 (v2) | 65.5 | # 10 | ||||
Emotion Recognition in Conversation | EmoryNLP | TUCORE-GCN_BERT | Weighted-F1 | 36.01 | # 23 | |
Emotion Recognition in Conversation | EmoryNLP | TUCORE-GCN_RoBERTa | Weighted-F1 | 39.24 | # 11 | |
Emotion Recognition in Conversation | MELD | TUCORE-GCN_BERT | Weighted-F1 | 62.47 | # 44 | |
Emotion Recognition in Conversation | MELD | TUCORE-GCN_RoBERTa | Weighted-F1 | 65.36 | # 31 |