Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction

• URL: http://arxiv.org/abs/2209.04112v1
• Date: Fri, 9 Sep 2022 04:06:27 GMT
• Title: Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction
• Authors: Shunjie Chen, Xiaochuan Shi, Jingye Li, Shengqiong Wu, Hao Fei, Fei Li, Donghong Ji
• Abstract summary: Emotion cause pair extraction (ECPE) is one of the derived subtasks of emotion cause analysis (ECA) ECPE shares rich inter-related features with emotion extraction (EE) and cause extraction (CE)
• Score: 36.123715709125015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotion cause pair extraction (ECPE), as one of the derived subtasks of emotion cause analysis (ECA), shares rich inter-related features with emotion extraction (EE) and cause extraction (CE). Therefore EE and CE are frequently utilized as auxiliary tasks for better feature learning, modeled via multi-task learning (MTL) framework by prior works to achieve state-of-the-art (SoTA) ECPE results. However, existing MTL-based methods either fail to simultaneously model the specific features and the interactive feature in between, or suffer from the inconsistency of label prediction. In this work, we consider addressing the above challenges for improving ECPE by performing two alignment mechanisms with a novel A^2Net model. We first propose a feature-task alignment to explicitly model the specific emotion-&cause-specific features and the shared interactive feature. Besides, an inter-task alignment is implemented, in which the label distance between the ECPE and the combinations of EE&CE are learned to be narrowed for better label consistency. Evaluations of benchmarks show that our methods outperform current best-performing systems on all ECA subtasks. Further analysis proves the importance of our proposed alignment mechanisms for the task.

Related papers

• Enhancing Complex Causality Extraction via Improved Subtask Interaction and Knowledge Fusion [35.14378353659475]
  Event Causality Extraction aims at extracting causal event pairs from texts. Existing fine-tuning based ECE methods cannot address all three key challenges in ECE simultaneously. We propose a unified ECE framework (UniCE) to address all three issues in ECE simultaneously.
  arXiv  Detail & Related papers  (2024-08-06T10:15:15Z)
• Emotion-cause pair extraction method based on multi-granularity information and multi-module interaction [0.6577148087211809]
  The purpose of emotion-cause pair extraction is to extract the pair of emotion clauses and cause clauses. Existing models do not adequately address the emotion and cause-induced locational imbalance of samples. We propose an end-to-end multitasking model (MM-ECPE) based on shared interaction between GRU, knowledge graph and transformer modules.
  arXiv  Detail & Related papers  (2024-04-10T08:00:26Z)
• Enhancing Large Language Model with Decomposed Reasoning for Emotion Cause Pair Extraction [13.245873138716044]
  Emotion-Cause Pair Extraction (ECPE) involves extracting clause pairs representing emotions and their causes in a document. Inspired by recent work, we explore leveraging large language model (LLM) to address ECPE task without additional training. We introduce chain-of-thought to mimic human cognitive process and propose the Decomposed Emotion-Cause Chain (DECC) framework.
  arXiv  Detail & Related papers  (2024-01-31T10:20:01Z)
• A Novel Energy based Model Mechanism for Multi-modal Aspect-Based Sentiment Analysis [85.77557381023617]
  We propose a novel framework called DQPSA for multi-modal sentiment analysis. PDQ module uses the prompt as both a visual query and a language query to extract prompt-aware visual information. EPE module models the boundaries pairing of the analysis target from the perspective of an Energy-based Model.
  arXiv  Detail & Related papers  (2023-12-13T12:00:46Z)
• Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis [72.9124467710526]
  generative approaches have been proposed to extract all four elements as (one or more) quadruplets from text as a single task. We propose a unified framework for solving ABSA, and the associated sub-tasks to improve the performance in few-shot scenarios.
  arXiv  Detail & Related papers  (2022-10-12T23:38:57Z)
• Attentive Max Feature Map for Acoustic Scene Classification with Joint Learning considering the Abstraction of Classes [48.6946385738143]
  We propose a mechanism referred to as the attentive max feature map which combines two effective techniques, attention and max feature map, to elaborate the attention mechanism and mitigate the phenomenon. Applying two proposed techniques, our proposed system achieves state-of-the-art performance among single systems on subtask A.
  arXiv  Detail & Related papers  (2021-04-15T03:14:15Z)
• An End-to-End Network for Emotion-Cause Pair Extraction [3.016628653955123]
  We propose an end-to-end model for the Emotion-Cause Pair Extraction (ECPE) task. Due to the unavailability of an English language ECPE corpus, we adapt the NTCIR-13 ECE corpus and establish a baseline for the ECPE task on this dataset.
  arXiv  Detail & Related papers  (2021-03-02T08:03:03Z)
• A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
  We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn) Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-04T14:59:32Z)
• An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis [73.7488524683061]
  We propose a novel Iterative Multi-Knowledge Transfer Network (IMKTN) for end-to-end ABSA. Our IMKTN transfers the task-specific knowledge from any two of the three subtasks to another one at the token level by utilizing a well-designed routing algorithm. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of our approach.
  arXiv  Detail & Related papers  (2020-04-04T13:49:54Z)
• End-to-end Emotion-Cause Pair Extraction via Learning to Link [18.741585103275334]
  Emotion-cause pair extraction (ECPE) aims at jointly investigating emotions and their underlying causes in documents. Existing approaches to ECPE generally adopt a two-stage method, i.e., (1) emotion and cause detection, and then (2) pairing the detected emotions and causes. We propose a multi-task learning model that can extract emotions, causes and emotion-cause pairs simultaneously in an end-to-end manner.
  arXiv  Detail & Related papers  (2020-02-25T07:49:12Z)

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.