# TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction

arXiv: https://arxiv.org/abs/2303.17119 · HTML: https://mlsanity.com/paper/2303.17119/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 30 Mar 2023.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019)
- [Extractive Summarization as Text Matching](https://mlsanity.com/paper/2004.08795/) (2020)
- [Reasoning with Latent Structure Refinement for Document-Level Relation Extraction](https://mlsanity.com/paper/2005.06312/) (2020)
- [Dialogue-Based Relation Extraction](https://mlsanity.com/paper/2004.08056/) (2020)
- [More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction](https://mlsanity.com/paper/2004.03186/) (2020)
- [Question Answering on Freebase via Relation Extraction and Textual Evidence](https://mlsanity.com/paper/1603.00957/) (2016)
- [GDPNet: Refining Latent Multi-View Graph for Relation Extraction](https://mlsanity.com/paper/2012.06780/) (2020)
- [Graph Based Network with Contextualized Representations of Turns in Dialogue](https://mlsanity.com/paper/2109.04008/) (2021)
- [LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling](https://mlsanity.com/paper/2210.11929/) (2022)
- [Learning Recurrent Span Representations for Extractive Question Answering](https://mlsanity.com/paper/1611.01436/) (2016)
- [An Embarrassingly Simple Model for Dialogue Relation Extraction](https://mlsanity.com/paper/2012.13873/) (2020)
- [Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks](https://mlsanity.com/paper/2009.05092/) (2020)
- [Towards Joint Intent Detection and Slot Filling via Higher-order Attention](https://mlsanity.com/paper/2109.08890/) (2021)
- [A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding](https://mlsanity.com/paper/2211.04023/) (2022)
- [A Dependency-Based Neural Network for Relation Classification](https://mlsanity.com/paper/1507.04646/) (2015)
- [Complex Relation Extraction: Challenges and Opportunities](https://mlsanity.com/paper/2012.04821/) (2020)
- [TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues](https://mlsanity.com/paper/2108.13811/) (2021)

## Abstract (arXiv)

Dialogue-based Relation Extraction (DRE) aims to predict the relation type of argument pairs that are mentioned in dialogue. The latest trigger-enhanced methods propose trigger prediction tasks to promote DRE. However, these methods are not able to fully leverage the trigger information and even bring noise to relation extraction. To solve these problems, we propose TLAG, which fully leverages the trigger and label-aware knowledge to guide the relation extraction. First, we design an adaptive trigger fusion module to fully leverage the trigger information. Then, we introduce label-aware knowledge to further promote our model's performance. Experimental results on the DialogRE dataset show that our TLAG outperforms the baseline models, and detailed analyses demonstrate the effectiveness of our approach.