Conversation Modeling to Predict Derailment

• URL: http://arxiv.org/abs/2303.11184v1
• Date: Mon, 20 Mar 2023 15:10:45 GMT
• Title: Conversation Modeling to Predict Derailment
• Authors: Jiaqing Yuan and Munindar P. Singh
• Abstract summary: 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.
• Score: 15.45515784064555
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conversations among online users sometimes derail, i.e., break down into personal attacks. Such derailment has a negative impact on the healthy growth of cyberspace communities. The ability to predict whether ongoing conversations are likely to derail could provide valuable real-time insight to interlocutors and moderators. Prior approaches predict conversation derailment retrospectively without the ability to forestall the derailment proactively. 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. We propose a domain-adaptive pretraining objective to integrate conversational structure information and a multitask learning scheme to leverage the distance from each utterance to derailment. An evaluation of our framework on two conversation derailment datasets yields improvement over F1 score for the prediction of derailment. These results demonstrate the effectiveness of incorporating multisource information.

Related papers

• Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
  Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
  arXiv  Detail & Related papers  (2024-11-07T21:37:51Z)
• 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)
• How Did We Get Here? Summarizing Conversation Dynamics [4.644319899528183]
  We introduce the task of summarizing the dynamics of conversations by constructing a dataset of human-written summaries. We evaluate whether such summaries can capture the trajectory of conversations via an established downstream task. We show that they help both humans and automated systems with this forecasting task.
  arXiv  Detail & Related papers  (2024-04-29T18:00:03Z)
• AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models [74.10293412011455]
  We propose AutoConv for synthetic conversation generation. Specifically, we formulate the conversation generation problem as a language modeling task. We finetune an LLM with a few human conversations to capture the characteristics of the information-seeking process.
  arXiv  Detail & Related papers  (2023-08-12T08:52:40Z)
• 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)
• Pre-training Multi-party Dialogue Models with Latent Discourse Inference [85.9683181507206]
  We pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model.
  arXiv  Detail & Related papers  (2023-05-24T14:06:27Z)
• FCTalker: Fine and Coarse Grained Context Modeling for Expressive Conversational Speech Synthesis [75.74906149219817]
  Conversational Text-to-Speech (TTS) aims to synthesis an utterance with the right linguistic and affective prosody in a conversational context. We propose a novel expressive conversational TTS model, as termed FCTalker, that learn the fine and coarse grained context dependency at the same time during speech generation.
  arXiv  Detail & Related papers  (2022-10-27T12:20:20Z)
• TANet: Thread-Aware Pretraining for Abstractive Conversational Summarization [27.185068253347257]
  We build a large-scale (11M) pretraining dataset called RCS based on the multi-person discussions in the Reddit community. We then present TANet, a thread-aware Transformer-based network. Unlike the existing pre-trained models that treat a conversation as a sequence of sentences, we argue that the inherent contextual dependency plays an essential role in understanding the entire conversation.
  arXiv  Detail & Related papers  (2022-04-09T16:08:46Z)
• Online Conversation Disentanglement with Pointer Networks [13.063606578730449]
  We propose an end-to-end online framework for conversation disentanglement. We design a novel way to embed the whole utterance that comprises timestamp, speaker, and message text. Our experiments on the Ubuntu IRC dataset show that our method achieves state-of-the-art performance in both link and conversation prediction tasks.
  arXiv  Detail & Related papers  (2020-10-21T15:43:07Z)
• Exploiting Unsupervised Data for Emotion Recognition in Conversations [76.01690906995286]
  Emotion Recognition in Conversations (ERC) aims to predict the emotional state of speakers in conversations. The available supervised data for the ERC task is limited. We propose a novel approach to leverage unsupervised conversation data.
  arXiv  Detail & Related papers  (2020-10-02T13:28:47Z)
• Dialogue Response Ranking Training with Large-Scale Human Feedback Data [52.12342165926226]
  We leverage social media feedback data to build a large-scale training dataset for feedback prediction. We trained DialogRPT, a set of GPT-2 based models on 133M pairs of human feedback data. Our ranker outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback.
  arXiv  Detail & Related papers  (2020-09-15T10:50:05Z)

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.