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Dialog Relation Extraction
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Dialog Relation Extraction is the task of predicting the relation type between entities mentioned in dialogue. It uses multiple tokens to capture possible relations between pairs of entities in the dialogue. The popular benchmark for this task is the DialogRE dataset. The models are typically evaluated with the metric of F1 Score for both standard-setting and conversational settings.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
GRASP: Guiding model with RelAtional Semantics using Prompt for Dialogue Relation Extraction
Document-Level Relation Extraction with Sentences Importance Estimation and Focusing
Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction
D-REX: Dialogue Relation Extraction with Explanations
Graph Based Network with Contextualized Representations of Turns in Dialogue
SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues
Semantic Representation for Dialogue Modeling
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
An Embarrassingly Simple Model for Dialogue Relation Extraction
GDPNet: Refining Latent Multi-View Graph for Relation Extraction
DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues
Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks
Dialogue-Based Relation Extraction
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