TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues
arXiv:2108.13811
Abstract
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.
Accepted to SIGDIAL 2022; The first two authors contributed to this work equally
References in corpus (5)
- Learning Recurrent Span Representations for Extractive Question Answering
- An Improved Baseline for Sentence-level Relation Extraction
- An Embarrassingly Simple Model for Dialogue Relation Extraction
- Relation Classification as Two-way Span-Prediction
- DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues