Re-entry Prediction for Online Conversations via Self-Supervised Learning

• URL: http://arxiv.org/abs/2109.02020v1
• Date: Sun, 5 Sep 2021 08:07:52 GMT
• Title: Re-entry Prediction for Online Conversations via Self-Supervised Learning
• Authors: Lingzhi Wang, Xingshan Zeng, Huang Hu, Kam-Fai Wong, Daxin Jiang
• Abstract summary: We propose three auxiliary tasks, namely, Spread Pattern, Repeated Target user, and Turn Authorship, as the self-supervised signals for re-entry prediction. Experimental results on two datasets newly collected from Twitter and Reddit show that our method outperforms the previous state-of-the-arts.
• Score: 25.488783376789026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, world business in online discussions and opinion sharing on social media is booming. Re-entry prediction task is thus proposed to help people keep track of the discussions which they wish to continue. Nevertheless, existing works only focus on exploiting chatting history and context information, and ignore the potential useful learning signals underlying conversation data, such as conversation thread patterns and repeated engagement of target users, which help better understand the behavior of target users in conversations. In this paper, we propose three interesting and well-founded auxiliary tasks, namely, Spread Pattern, Repeated Target user, and Turn Authorship, as the self-supervised signals for re-entry prediction. These auxiliary tasks are trained together with the main task in a multi-task manner. Experimental results on two datasets newly collected from Twitter and Reddit show that our method outperforms the previous state-of-the-arts with fewer parameters and faster convergence. Extensive experiments and analysis show the effectiveness of our proposed models and also point out some key ideas in designing self-supervised tasks.

Related papers

• NewsDialogues: Towards Proactive News Grounded Conversation [72.10055780635625]
  We propose a novel task, Proactive News Grounded Conversation, in which a dialogue system can proactively lead the conversation based on some key topics of the news. To further develop this novel task, we collect a human-to-human Chinese dialogue dataset tsNewsDialogues, which includes 1K conversations with a total of 14.6K utterances.
  arXiv  Detail & Related papers  (2023-08-12T08:33:42Z)
• "You might think about slightly revising the title": identifying hedges in peer-tutoring interactions [1.0466434989449724]
  Hedges play an important role in the management of conversational interaction. We use a multimodal peer-tutoring dataset to construct a computational framework for identifying hedges. We employ a model explainability tool to explore the features that characterize hedges in peer-tutoring conversations.
  arXiv  Detail & Related papers  (2023-06-18T12:47:54Z)
• Task Compass: Scaling Multi-task Pre-training with Task Prefix [122.49242976184617]
  Existing studies show that multi-task learning with large-scale supervised tasks suffers from negative effects across tasks. We propose a task prefix guided multi-task pre-training framework to explore the relationships among tasks. Our model can not only serve as the strong foundation backbone for a wide range of tasks but also be feasible as a probing tool for analyzing task relationships.
  arXiv  Detail & Related papers  (2022-10-12T15:02:04Z)
• KETOD: Knowledge-Enriched Task-Oriented Dialogue [77.59814785157877]
  Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. We investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model.
  arXiv  Detail & Related papers  (2022-05-11T16:01:03Z)
• 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)
• Unsupervised Conversation Disentanglement through Co-Training [30.304609312675186]
  We explore to train a conversation disentanglement model without referencing any human annotations. Our method is built upon a deep co-training algorithm, which consists of two neural networks. For the message-pair classifier, we enrich its training data by retrieving message pairs with high confidence.
  arXiv  Detail & Related papers  (2021-09-07T17:05:18Z)
• Parrot: Data-Driven Behavioral Priors for Reinforcement Learning [79.32403825036792]
  We propose a method for pre-training behavioral priors that can capture complex input-output relationships observed in successful trials. We show how this learned prior can be used for rapidly learning new tasks without impeding the RL agent's ability to try out novel behaviors.
  arXiv  Detail & Related papers  (2020-11-19T18:47:40Z)
• Probing Task-Oriented Dialogue Representation from Language Models [106.02947285212132]
  This paper investigates pre-trained language models to find out which model intrinsically carries the most informative representation for task-oriented dialogue tasks. We fine-tune a feed-forward layer as the classifier probe on top of a fixed pre-trained language model with annotated labels in a supervised way.
  arXiv  Detail & Related papers  (2020-10-26T21:34:39Z)
• Adding Chit-Chat to Enhance Task-Oriented Dialogues [36.93917437554091]
  Chit-Chat can be added to task-oriented dialogues to make virtual assistant conversations more engaging and interactive. We present our new chit-chat-based annotations to 23.8K dialogues from two popular task-oriented dialogue datasets. We also propose three new models for adding chit-chat to task-oriented dialogues, explicitly trained to predict user goals and to generate contextually relevant chit-chat responses.
  arXiv  Detail & Related papers  (2020-10-24T03:22:43Z)
• Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues [88.73739515457116]
  We introduce four self-supervised tasks including next session prediction, utterance restoration, incoherence detection and consistency discrimination. We jointly train the PLM-based response selection model with these auxiliary tasks in a multi-task manner. Experiment results indicate that the proposed auxiliary self-supervised tasks bring significant improvement for multi-turn response selection.
  arXiv  Detail & Related papers  (2020-09-14T08:44:46Z)

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.