Knowledge-Aware Conversation Derailment Forecasting Using Graph Convolutional Networks

• URL: http://arxiv.org/abs/2408.13440v2
• Date: Sun, 8 Sep 2024 21:47:29 GMT
• Title: Knowledge-Aware Conversation Derailment Forecasting Using Graph Convolutional Networks
• Authors: Enas Altarawneh, Ameeta Agrawal, Michael Jenkin, Manos Papagelis,
• Abstract summary: 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.
• Score: 5.571668670990489
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Online conversations are particularly susceptible to derailment, which can manifest itself in the form of toxic communication patterns including disrespectful comments and abuse. Forecasting conversation derailment predicts signs of derailment in advance enabling proactive moderation of conversations. State-of-the-art approaches to conversation derailment forecasting sequentially encode conversations and use graph neural networks to model dialogue user dynamics. However, existing graph models are not able to capture complex conversational characteristics such as context propagation and emotional shifts. The use of common sense knowledge enables a model to capture such characteristics, thus improving performance. Following this approach, here 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

Related papers

• WavChat: A Survey of Spoken Dialogue Models [66.82775211793547]
  Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain. These advanced spoken dialogue models not only comprehend audio, music, and other speech-related features, but also capture stylistic and timbral characteristics in speech. Despite the progress in spoken dialogue systems, there is a lack of comprehensive surveys that systematically organize and analyze these systems.
  arXiv  Detail & Related papers  (2024-11-15T04:16:45Z)
• Yeah, Un, Oh: Continuous and Real-time Backchannel Prediction with Fine-tuning of Voice Activity Projection [24.71649541757314]
  Short backchannel utterances such as "yeah" and "oh" play a crucial role in facilitating smooth and engaging dialogue. This paper proposes a novel method for real-time, continuous backchannel prediction using a fine-tuned Voice Activity Projection model.
  arXiv  Detail & Related papers  (2024-10-21T11:57:56Z)
• LineConGraphs: Line Conversation Graphs for Effective Emotion Recognition using Graph Neural Networks [10.446376560905863]
  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%.
  arXiv  Detail & Related papers  (2023-12-04T19:36:58Z)
• 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)
• PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model [79.64376762489164]
  PK-Chat is a Pointer network guided generative dialogue model, incorporating a unified pretrained language model and a pointer network over knowledge graphs. The words generated by PK-Chat in the dialogue are derived from the prediction of word lists and the direct prediction of the external knowledge graph knowledge. Based on the PK-Chat, a dialogue system is built for academic scenarios in the case of geosciences.
  arXiv  Detail & Related papers  (2023-04-02T18:23:13Z)
• Conversation Modeling to Predict Derailment [15.45515784064555]
  The ability to predict whether ongoing conversations are likely to derail could provide valuable real-time insight to interlocutors and moderators. Some works attempt to make dynamic prediction as the conversation develops, but fail to incorporate multisource information, such as conversation structure and distance to derailment. We propose a hierarchical transformer-based framework that combines utterance-level and conversation-level information to capture fine-grained contextual semantics.
  arXiv  Detail & Related papers  (2023-03-20T15:10:45Z)
• Improving a sequence-to-sequence nlp model using a reinforcement learning policy algorithm [0.0]
  Current neural network models of dialogue generation show great promise for generating answers for chatty agents. But they are short-sighted in that they predict utterances one at a time while disregarding their impact on future outcomes. This work commemorates a preliminary step toward developing a neural conversational model based on the long-term success of dialogues.
  arXiv  Detail & Related papers  (2022-12-28T22:46:57Z)
• Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues [73.04906599884868]
  We propose a novel framework of Reasoning Paths in Dialogue Context (PDC) PDC model discovers information flows among dialogue turns through a semantic graph constructed based on lexical components in each question and answer. Our model sequentially processes both visual and textual information through this reasoning path and the propagated features are used to generate the answer.
  arXiv  Detail & Related papers  (2021-03-01T07:39:26Z)
• 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)
• Ranking Enhanced Dialogue Generation [77.8321855074999]
  How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures to model the history. This paper proposes a Ranking Enhanced Dialogue generation framework.
  arXiv  Detail & Related papers  (2020-08-13T01:49:56Z)
• A Controllable Model of Grounded Response Generation [122.7121624884747]
  Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process. We propose a framework that we call controllable grounded response generation (CGRG) We show that using this framework, a transformer based model with a novel inductive attention mechanism, trained on a conversation-like Reddit dataset, outperforms strong generation baselines.
  arXiv  Detail & Related papers  (2020-05-01T21:22:08Z)

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.