TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues

• URL: http://arxiv.org/abs/2108.13811v1
• Date: Tue, 31 Aug 2021 13:04:08 GMT
• Title: TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues
• Authors: Po-Wei Lin, Shang-Yu Su, Yun-Nung Chen
• Abstract summary: This paper proposes TREND, a multi-tasking BERT-based model which learns to identify triggers for improving relation extraction. The experimental results show that the proposed method achieves the state-of-the-art on the benchmark datasets.
• Score: 37.883583724569554
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 some clues, such evidences called "triggers". However, none of the existing work on DRE tried to detect triggers and leverage the information for enhancing the performance. This paper proposes TREND, a multi-tasking BERT-based model which learns to identify triggers for improving relation extraction. The experimental results show that the proposed method achieves the state-of-the-art on the benchmark datasets.

Related papers

• Zero-Shot Dialogue Relation Extraction by Relating Explainable Triggers and Relation Names [28.441725610692714]
  This paper proposes a method for leveraging the ability to capture triggers and relate them to previously unseen relation names. Our experiments on a benchmark DialogRE dataset demonstrate that the proposed model achieves significant improvements for both seen and unseen relations.
  arXiv  Detail & Related papers  (2023-06-09T07:10:01Z)
• TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction [43.98737106776455]
  Dialogue-based Relation Extraction (DRE) aims to predict the relation type of argument pairs that are mentioned in dialogue. We propose TLAG, which fully leverages the trigger and label-aware knowledge to guide the relation extraction. Experimental results on the DialogRE dataset show that our TLAG outperforms the baseline models.
  arXiv  Detail & Related papers  (2023-03-30T03:10:28Z)
• GRASP: Guiding model with RelAtional Semantics using Prompt [3.1275060062551208]
  We propose a Guiding model with RelAtional Semantics using Prompt (GRASP) We adopt a prompt-based fine-tuning approach and capture relational semantic clues of a given dialogue with an argument-aware prompt marker strategy. In the experiments, GRASP state-of-the-art performance in terms of both F1 and F1c scores on a DialogRE dataset.
  arXiv  Detail & Related papers  (2022-08-26T08:19:28Z)
• D-REX: Dialogue Relation Extraction with Explanations [65.3862263565638]
  This work focuses on extracting explanations that indicate that a relation exists while using only partially labeled data. We propose our model-agnostic framework, D-REX, a policy-guided semi-supervised algorithm that explains and ranks relations. We find that about 90% of the time, human annotators prefer D-REX's explanations over a strong BERT-based joint relation extraction and explanation model.
  arXiv  Detail & Related papers  (2021-09-10T22:30:48Z)
• An Embarrassingly Simple Model for Dialogue Relation Extraction [44.2379205657313]
  We model Dialogue RE as a multi-label classification task and propose a simple yet effective model named SimpleRE. SimpleRE captures the interrelations among multiple relations in a dialogue through a novel input format, BERT Relation Token Sequence (BRS) A Relation Refinement Gate (RRG) is designed to extract relation-specific semantic representation adaptively.
  arXiv  Detail & Related papers  (2020-12-27T06:22:23Z)
• Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks [61.950353376870154]
  Joint-event-extraction is a sequence-to-sequence labeling task with a tag set composed of tags of triggers and entities. We propose a Cross-Supervised Mechanism (CSM) to alternately supervise the extraction of triggers or entities. Our approach outperforms the state-of-the-art methods in both entity and trigger extraction.
  arXiv  Detail & Related papers  (2020-10-13T11:51:17Z)
• DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification [77.59549450705384]
  In dialog system, dialog act recognition and sentiment classification are two correlative tasks. Most of the existing systems either treat them as separate tasks or just jointly model the two tasks. We propose a Deep Co-Interactive Relation Network (DCR-Net) to explicitly consider the cross-impact and model the interaction between the two tasks.
  arXiv  Detail & Related papers  (2020-08-16T14:13:32Z)
• Dialogue-Based Relation Extraction [53.2896545819799]
  We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings.
  arXiv  Detail & Related papers  (2020-04-17T03:51:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.