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

• URL: http://arxiv.org/abs/2303.17119v1
• Date: Thu, 30 Mar 2023 03:10:28 GMT
• Title: TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction
• Authors: Hao An, Dongsheng Chen, Weiyuan Xu, Zhihong Zhu, Yuexian Zou
• Abstract summary: 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.
• Score: 43.98737106776455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Chain of Thought Explanation for Dialogue State Tracking [52.015771676340016]
  Dialogue state tracking (DST) aims to record user queries and goals during a conversational interaction. We propose a model named Chain-of-Thought-Explanation (CoTE) for the DST task. CoTE is designed to create detailed explanations step by step after determining the slot values.
  arXiv  Detail & Related papers  (2024-03-07T16:59:55Z)
• 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)
• Message Intercommunication for Inductive Relation Reasoning [49.731293143079455]
  We develop a novel inductive relation reasoning model called MINES. We introduce a Message Intercommunication mechanism on the Neighbor-Enhanced Subgraph. Our experiments show that MINES outperforms existing state-of-the-art models.
  arXiv  Detail & Related papers  (2023-05-23T13:51:46Z)
• 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)
• CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction [22.746071199667146]
  Implicit event argument extraction (EAE) aims to identify arguments that could scatter over the document. We propose a Curriculum learning based Prompt tuning (CUP) approach, which resolves implicit EAE by four learning stages. In addition, we integrate a prompt-based encoder-decoder model to elicit related knowledge from pre-trained language models.
  arXiv  Detail & Related papers  (2022-05-01T16:03:54Z)
• SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction [51.27558374091491]
  We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
  arXiv  Detail & Related papers  (2021-09-24T17:37:35Z)
• 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)
• AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction [54.20039200180071]
  We present a novel framework to improve NER performance by automatically generating and leveraging entity triggers'' Our framework leverages post-hoc explanation to generate rationales and strengthens a model's prior knowledge using an embedding technique. AutoTriggER shows strong label-efficiency, is capable of generalizing to unseen entities, and outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on average.
  arXiv  Detail & Related papers  (2021-09-10T08:11:56Z)
• TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues [37.883583724569554]
  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.
  arXiv  Detail & Related papers  (2021-08-31T13:04:08Z)
• A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset [8.990035371365408]
  We introduce FastSGT, a fast and robust BERT-based model for state tracking in goal-oriented dialogue systems. The proposed model is designed for theGuided Dialogue dataset which contains natural language descriptions. Our model keeps the efficiency in terms of computational and memory consumption while improving the accuracy significantly.
  arXiv  Detail & Related papers  (2020-08-27T18:51:18Z)

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.