# TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues

arXiv: https://arxiv.org/abs/2108.13811 · HTML: https://mlsanity.com/paper/2108.13811/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 19 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [Reasoning with Latent Structure Refinement for Document-Level Relation Extraction](https://mlsanity.com/paper/2005.06312/) (2020)
- [An Improved Baseline for Sentence-level Relation Extraction](https://mlsanity.com/paper/2102.01373/) (2021)
- [Dialogue-Based Relation Extraction](https://mlsanity.com/paper/2004.08056/) (2020)
- [GDPNet: Refining Latent Multi-View Graph for Relation Extraction](https://mlsanity.com/paper/2012.06780/) (2020)
- [Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning](https://mlsanity.com/paper/1801.06176/) (2018)
- [Learning Recurrent Span Representations for Extractive Question Answering](https://mlsanity.com/paper/1611.01436/) (2016)
- [DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues](https://mlsanity.com/paper/2012.02553/) (2020)
- [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)
- [Natural Language Generation by Hierarchical Decoding with Linguistic Patterns](https://mlsanity.com/paper/1808.02747/) (2018)
- [Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning](https://mlsanity.com/paper/1808.09442/) (2018)

## Built on by (most cited first, 3 of 3)

- [Are Triggers Needed for Document-Level Event Extraction?](https://mlsanity.com/paper/2411.08708/) (2024)
- [TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction](https://mlsanity.com/paper/2303.17119/) (2023)
- [Zero-Shot Dialogue Relation Extraction by Relating Explainable Triggers and Relation Names](https://mlsanity.com/paper/2306.06141/) (2023)

## Abstract (arXiv)

The goal of dialogue relation extraction (DRE) is to identify the relation between two entities in a given dialogue. During conversations, speakers may expose their relations to certain entities by explicit or implicit clues, such evidences called "triggers". However, trigger annotations may not be always available for the target data, so it is challenging to leverage such information for enhancing the performance. Therefore, this paper proposes to learn how to identify triggers from the data with trigger annotations and then transfers the trigger-finding capability to other datasets for better performance. The experiments show that the proposed approach is capable of improving relation extraction performance of unseen relations and also demonstrate the transferability of our proposed trigger-finding model across different domains and datasets.