Dialog Relation Extraction
13 papers with code • 2 benchmarks • 2 datasets
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.
DialogRE
DDRel
Most implemented papers
Document-Level Relation Extraction with Sentences Importance Estimation and Focusing
xwjim/sief
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NAACL 2022
Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences.
Global inference with explicit syntactic and discourse structures for dialogue-level relation extraction
scofield7419/DiaRE-D2G
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Conference 2022
In our global reasoning framework, D2G and ARG work collaboratively, iteratively performing lexical, syntactic and semantic information exchange and representation learning over the entire dialogue context.
GRASP: Guiding model with RelAtional Semantics using Prompt for Dialogue Relation Extraction
To effectively exploit inherent knowledge of PLMs without extra layers and consider scattered semantic cues on the relation between the arguments, we propose a Guiding model with RelAtional Semantics using Prompt (GRASP).
