MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation

• URL: http://arxiv.org/abs/2305.12733v2
• Date: Wed, 18 Oct 2023 02:09:24 GMT
• Title: MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation
• Authors: Jia-Chen Gu, Chao-Hong Tan, Caiyuan Chu, Zhen-Hua Ling, Chongyang Tao, Quan Liu, Cong Liu
• Abstract summary: We study the scarcity of addressee labels which is a common issue in multi-party conversations (MPCs) We propose MADNet that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation. Experimental results on two Ubuntu IRC channel benchmarks show that MADNet outperforms various baseline models on the task of MPC generation.
• Score: 64.54727792762816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling multi-party conversations (MPCs) with graph neural networks has been proven effective at capturing complicated and graphical information flows. However, existing methods rely heavily on the necessary addressee labels and can only be applied to an ideal setting where each utterance must be tagged with an addressee label. To study the scarcity of addressee labels which is a common issue in MPCs, we propose MADNet that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation. Given an MPC with a few addressee labels missing, existing methods fail to build a consecutively connected conversation graph, but only a few separate conversation fragments instead. To ensure message passing between these conversation fragments, four additional types of latent edges are designed to complete a fully-connected graph. Besides, to optimize the edge-type-dependent message passing for those utterances without addressee labels, an Expectation-Maximization-based method that iteratively generates silver addressee labels (E step), and optimizes the quality of generated responses (M step), is designed. Experimental results on two Ubuntu IRC channel benchmarks show that MADNet outperforms various baseline models on the task of MPC generation, especially under the more common and challenging setting where part of addressee labels are missing.

Related papers

• Task-Oriented Communication for Graph Data: A Graph Information Bottleneck Approach [12.451324619122405]
  This paper introduces a method to extract a smaller, task-focused subgraph that maintains key information while reducing communication overhead. Our approach utilizes graph neural networks (GNNs) and the graph information bottleneck (GIB) principle to create a compact, informative, and robust graph representation suitable for transmission.
  arXiv  Detail & Related papers  (2024-09-04T14:01:56Z)
• GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understanding [51.37738394062851]
  GIFT can adapt various Transformer-based pre-trained language models for universal MPC understanding. Four types of edges are designed to integrate graph-induced signals into attention mechanisms.
  arXiv  Detail & Related papers  (2023-05-16T11:35:24Z)
• Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels [60.675714333081466]
  Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, ie, CLIP, to compensate for insufficient annotations. We advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior.
  arXiv  Detail & Related papers  (2023-03-23T12:39:20Z)
• Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks [18.104982772430102]
  We propose a novel method called Partial Message Meta Multigraph search (PMMM) to automatically optimize the neural architecture design on Heterogeneous Information Networks (HINs) PMMM adopts an efficient differentiable framework to search for a meaningful meta multigraph, which can capture more flexible and complex semantic relations than a meta graph. Our approach outperforms the state-of-the-art heterogeneous GNNs, finds out meaningful meta multigraphs, and is significantly more stable.
  arXiv  Detail & Related papers  (2022-11-27T07:35:42Z)
• EGG-GAE: scalable graph neural networks for tabular data imputation [8.775728170359024]
  We propose a novel EdGe Generation Graph AutoEncoder (EGG-GAE) for missing data imputation. EGG-GAE works on randomly sampled mini-batches of the input data, and it automatically infers the best connectivity across the mini-batch for each architecture layer.
  arXiv  Detail & Related papers  (2022-10-19T10:26:17Z)
• Dynamic Graph Message Passing Networks for Visual Recognition [112.49513303433606]
  Modelling long-range dependencies is critical for scene understanding tasks in computer vision. A fully-connected graph is beneficial for such modelling, but its computational overhead is prohibitive. We propose a dynamic graph message passing network, that significantly reduces the computational complexity.
  arXiv  Detail & Related papers  (2022-09-20T14:41:37Z)
• Affinity-Aware Graph Networks [9.888383815189176]
  Graph Neural Networks (GNNs) have emerged as a powerful technique for learning on relational data. We explore the use of affinity measures as features in graph neural networks. We propose message passing networks based on these features and evaluate their performance on a variety of node and graph property prediction tasks.
  arXiv  Detail & Related papers  (2022-06-23T18:51:35Z)
• GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification [23.79498916023468]
  Extreme multi-label text classification (XMTC) aims to tag a text instance with the most relevant subset of labels from an extremely large label set. GNN-XML is a scalable graph neural network framework tailored for XMTC problems.
  arXiv  Detail & Related papers  (2020-12-10T18:18:34Z)
• Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition [75.44233392355711]
  KGGR framework exploits prior knowledge of statistical label correlations with deep neural networks. It first builds a structured knowledge graph to correlate different labels based on statistical label co-occurrence. Then, it introduces the label semantics to guide learning semantic-specific features. It exploits a graph propagation network to explore graph node interactions.
  arXiv  Detail & Related papers  (2020-09-20T15:05:29Z)
• Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization [77.21951145754065]
  We propose a novel Cross- and Self-Modal Graph Attention Network (CSMGAN) that recasts this task as a process of iterative messages passing over a joint graph. Our CSMGAN is able to effectively capture high-order interactions between two modalities, thus enabling a further precise localization.
  arXiv  Detail & Related papers  (2020-08-04T08:25:24Z)

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.