Dialog Relation Extraction
11 papers with code • 2 benchmarks • 2 datasets
This graph is fed to a graph attention network for context propagation among relevant nodes, which effectively captures the dialogue context.
In this paper, we propose the task of relation classification of interlocutors based on their dialogues.
Recent advances on RE task are from BERT-based sequence modeling and graph-based modeling of relationships among the tokens in the sequence.
To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt).
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.
Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods.
Dialogue-based relation extraction (DiaRE) aims to detect the structural information from unstructured utterances in dialogues.