LineConGraphs: Line Conversation Graphs for Effective Emotion Recognition using Graph Neural Networks

• URL: http://arxiv.org/abs/2312.03756v1
• Date: Mon, 4 Dec 2023 19:36:58 GMT
• Title: LineConGraphs: Line Conversation Graphs for Effective Emotion Recognition using Graph Neural Networks
• Authors: Gokul S Krishnan, Sarala Padi, Craig S. Greenberg, Balaraman Ravindran, Dinesh Manoch and Ram D.Sriram
• Abstract summary: We propose novel line conversation graph convolutional network (LineConGCN) and graph attention (LineConGAT) models for Emotion Recognition in Conversations (ERC) analysis. These models are speaker-independent and built using a graph construction strategy for conversations -- line conversation graphs (LineConGraphs) We evaluate the performance of our proposed models on two benchmark datasets, IEMOCAP and MELD, and show that our LineConGAT model outperforms the state-of-the-art methods with an F1-score of 64.58% and 76.50%.
• Score: 10.446376560905863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emotion Recognition in Conversations (ERC) is a critical aspect of affective computing, and it has many practical applications in healthcare, education, chatbots, and social media platforms. Earlier approaches for ERC analysis involved modeling both speaker and long-term contextual information using graph neural network architectures. However, it is ideal to deploy speaker-independent models for real-world applications. Additionally, long context windows can potentially create confusion in recognizing the emotion of an utterance in a conversation. To overcome these limitations, we propose novel line conversation graph convolutional network (LineConGCN) and graph attention (LineConGAT) models for ERC analysis. These models are speaker-independent and built using a graph construction strategy for conversations -- line conversation graphs (LineConGraphs). The conversational context in LineConGraphs is short-term -- limited to one previous and future utterance, and speaker information is not part of the graph. We evaluate the performance of our proposed models on two benchmark datasets, IEMOCAP and MELD, and show that our LineConGAT model outperforms the state-of-the-art methods with an F1-score of 64.58% and 76.50%. Moreover, we demonstrate that embedding sentiment shift information into line conversation graphs further enhances the ERC performance in the case of GCN models.

Related papers

• Knowledge-Aware Conversation Derailment Forecasting Using Graph Convolutional Networks [5.571668670990489]
  We derive commonsense statements from a knowledge base of dialogue contextual information to enrich a graph neural network classification architecture. We fuse the multi-source information on utterance into capsules, which are used by a transformer-based forecaster to predict conversation derailment. Our model captures conversation dynamics and context propagation, outperforming the state-of-the-art models on the CGA and CMV benchmark datasets.
  arXiv  Detail & Related papers  (2024-08-24T02:40:28Z)
• G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering [61.93058781222079]
  We develop a flexible question-answering framework targeting real-world textual graphs. We introduce the first retrieval-augmented generation (RAG) approach for general textual graphs. G-Retriever performs RAG over a graph by formulating this task as a Prize-Collecting Steiner Tree optimization problem.
  arXiv  Detail & Related papers  (2024-02-12T13:13:04Z)
• TOD-Flow: Modeling the Structure of Task-Oriented Dialogues [77.15457469745364]
  We propose a novel approach focusing on inferring the TOD-Flow graph from dialogue data annotated with dialog acts. The inferred TOD-Flow graph can be easily integrated with any dialogue model to improve its prediction performance, transparency, and controllability.
  arXiv  Detail & Related papers  (2023-12-07T20:06:23Z)
• Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation [23.54754465832362]
  In conventional graph neural networks (GNNs) message passing on a graph is independent from text. This training regime leads to a semantic gap between graph knowledge and text. We propose a novel framework for knowledge graph enhanced dialogue generation.
  arXiv  Detail & Related papers  (2023-06-28T13:21:00Z)
• Conversation Derailment Forecasting with Graph Convolutional Networks [6.251188655534379]
  We propose a novel model based on a graph convolutional neural network that considers dialogue user dynamics and the influence of public perception on conversation utterances. Our model effectively captures conversation dynamics and outperforms the state-of-the-art models on the CGA and CMV benchmark datasets by 1.5% and 1.7%, respectively.
  arXiv  Detail & Related papers  (2023-06-22T15:40:59Z)
• GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph [53.70520466556453]
  We propose GraphFormers, where layerwise GNN components are nested alongside the transformer blocks of language models. With the proposed architecture, the text encoding and the graph aggregation are fused into an iterative workflow. In addition, a progressive learning strategy is introduced, where the model is successively trained on manipulated data and original data to reinforce its capability of integrating information on graph.
  arXiv  Detail & Related papers  (2021-05-06T12:20:41Z)
• GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems [133.13117064357425]
  We propose a new evaluation metric GRADE, which stands for Graph-enhanced Representations for Automatic Dialogue Evaluation. Specifically, GRADE incorporates both coarse-grained utterance-level contextualized representations and fine-grained topic-level graph representations to evaluate dialogue coherence. Experimental results show that our GRADE significantly outperforms other state-of-the-art metrics on measuring diverse dialogue models.
  arXiv  Detail & Related papers  (2020-10-08T14:07:32Z)
• Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks [21.409522845011907]
  Dialogue relation extraction (DRE) aims to detect the relation between two entities mentioned in a multi-party dialogue. We present a graph attention network-based method for DRE where a graph contains meaningfully connected speaker, entity, entity-type, and utterance nodes. We empirically show that this graph-based approach quite effectively captures the relations between different entity pairs in a dialogue as it outperforms the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-09-10T18:51:48Z)
• Compact Graph Architecture for Speech Emotion Recognition [0.0]
  A compact, efficient and scalable way to represent data is in the form of graphs. We construct a Graph Convolution Network (GCN)-based architecture that can perform an accurate graph convolution. Our model achieves comparable performance to the state-of-the-art with significantly fewer learnable parameters.
  arXiv  Detail & Related papers  (2020-08-05T12:09:09Z)
• A Graph-based Interactive Reasoning for Human-Object Interaction Detection [71.50535113279551]
  We present a novel graph-based interactive reasoning model called Interactive Graph (abbr. in-Graph) to infer HOIs. We construct a new framework to assemble in-Graph models for detecting HOIs, namely in-GraphNet. Our framework is end-to-end trainable and free from costly annotations like human pose.
  arXiv  Detail & Related papers  (2020-07-14T09:29:03Z)
• Iterative Context-Aware Graph Inference for Visual Dialog [126.016187323249]
  We propose a novel Context-Aware Graph (CAG) neural network. Each node in the graph corresponds to a joint semantic feature, including both object-based (visual) and history-related (textual) context representations.
  arXiv  Detail & Related papers  (2020-04-05T13:09:37Z)

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.