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.
Most implemented papers
Dialogue-Based Relation Extraction
We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue.
Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks
This graph is fed to a graph attention network for context propagation among relevant nodes, which effectively captures the dialogue context.
DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues
In this paper, we propose the task of relation classification of interlocutors based on their dialogues.
GDPNet: Refining Latent Multi-View Graph for Relation Extraction
Recent advances on RE task are from BERT-based sequence modeling and graph-based modeling of relationships among the tokens in the sequence.
An Embarrassingly Simple Model for Dialogue Relation Extraction
Dialogue relation extraction (RE) is to predict the relation type of two entities mentioned in a dialogue.
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
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).
Semantic Representation for Dialogue Modeling
Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities.
Graph Based Network with Contextualized Representations of Turns in Dialogue
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.
D-REX: Dialogue Relation Extraction with Explanations
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.
Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction
Dialogue-based relation extraction (DiaRE) aims to detect the structural information from unstructured utterances in dialogues.