# An Embarrassingly Simple Model for Dialogue Relation Extraction

arXiv: https://arxiv.org/abs/2012.13873 · HTML: https://mlsanity.com/paper/2012.13873/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2020.

## Built on by (most cited first, 13 of 13)

- [Think before you speak: Training Language Models With Pause Tokens](https://mlsanity.com/paper/2310.02226/) (2023)
- [Sequence Parallelism: Long Sequence Training from System Perspective](https://mlsanity.com/paper/2105.13120/) (2021)
- [Go Wider Instead of Deeper](https://mlsanity.com/paper/2107.11817/) (2021)
- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [Multi-CLS BERT: An Efficient Alternative to Traditional Ensembling](https://mlsanity.com/paper/2210.05043/) (2022)
- [SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues](https://mlsanity.com/paper/2106.01006/) (2021)
- [Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction](https://mlsanity.com/paper/2306.11020/) (2023)
- [Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention](https://mlsanity.com/paper/2305.00262/) (2023)
- [Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling](https://mlsanity.com/paper/2412.02415/) (2024)
- [A Study on Transformer Configuration and Training Objective](https://mlsanity.com/paper/2205.10505/) (2022)
- [TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues](https://mlsanity.com/paper/2108.13811/) (2021)
- [Variational Multi-Modal Hypergraph Attention Network for Multi-Modal Relation Extraction](https://mlsanity.com/paper/2404.12006/) (2024)
- [TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction](https://mlsanity.com/paper/2303.17119/) (2023)

## Abstract (arXiv)

Dialogue relation extraction (RE) is to predict the relation type of two entities mentioned in a dialogue. In this paper, we propose a simple yet effective model named SimpleRE for the RE task. SimpleRE captures the interrelations among multiple relations in a dialogue through a novel input format named BERT Relation Token Sequence (BRS). In BRS, multiple [CLS] tokens are used to capture possible relations between different pairs of entities mentioned in the dialogue. A Relation Refinement Gate (RRG) is then designed to extract relation-specific semantic representation in an adaptive manner. Experiments on the DialogRE dataset show that SimpleRE achieves the best performance, with much shorter training time. Further, SimpleRE outperforms all direct baselines on sentence-level RE without using external resources.