Mind the Gap Between Conversations for Improved Long-Term Dialogue Generation

• URL: http://arxiv.org/abs/2310.15415v1
• Date: Tue, 24 Oct 2023 00:12:38 GMT
• Title: Mind the Gap Between Conversations for Improved Long-Term Dialogue Generation
• Authors: Qiang Zhang, Jason Naradowsky, Yusuke Miyao
• Abstract summary: GapChat is a multi-session dialogue dataset in which the time between each session varies. While the dataset is constructed in real-time, progress on events in speakers' lives is simulated in order to create realistic dialogues occurring across a long timespan. We show that time-aware models perform better in metrics that judge the relevance of the chosen topics and the information gained from the conversation.
• Score: 21.109006148673846
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Knowing how to end and resume conversations over time is a natural part of communication, allowing for discussions to span weeks, months, or years. The duration of gaps between conversations dictates which topics are relevant and which questions to ask, and dialogue systems which do not explicitly model time may generate responses that are unnatural. In this work we explore the idea of making dialogue models aware of time, and present GapChat, a multi-session dialogue dataset in which the time between each session varies. While the dataset is constructed in real-time, progress on events in speakers' lives is simulated in order to create realistic dialogues occurring across a long timespan. We expose time information to the model and compare different representations of time and event progress. In human evaluation we show that time-aware models perform better in metrics that judge the relevance of the chosen topics and the information gained from the conversation.

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)
• Evaluating Very Long-Term Conversational Memory of LLM Agents [95.84027826745609]
  We introduce a machine-human pipeline to generate high-quality, very long-term dialogues. We equip each agent with the capability of sharing and reacting to images. The generated conversations are verified and edited by human annotators for long-range consistency.
  arXiv  Detail & Related papers  (2024-02-27T18:42:31Z)
• Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations [9.249662593315541]
  We introduce a new 1M multi-session dialogue dataset, Conversation Chronicles, for implementing a long-term conversation setup. We show that dialogue episodes in Conversation Chronicles reflect those properties while maintaining coherent and consistent interactions. We also propose a dialogue model, called ReBot, which consists of chronological summarization and dialogue generation modules.
  arXiv  Detail & Related papers  (2023-10-20T11:06:21Z)
• Multi-turn Dialogue Comprehension from a Topic-aware Perspective [70.37126956655985]
  This paper proposes to model multi-turn dialogues from a topic-aware perspective. We use a dialogue segmentation algorithm to split a dialogue passage into topic-concentrated fragments in an unsupervised way. We also present a novel model, Topic-Aware Dual-Attention Matching (TADAM) Network, which takes topic segments as processing elements.
  arXiv  Detail & Related papers  (2023-09-18T11:03:55Z)
• History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System [59.78425104243993]
  We propose History-Aware Hierarchical Transformer (HAHT) for multi-session open-domain dialogue. HAHT maintains a long-term memory of history conversations and utilizes history information to understand current conversation context. Experimental results on a large-scale Multi-Session Conversation dataset suggest that the proposed HAHT model consistently outperforms baseline models.
  arXiv  Detail & Related papers  (2023-02-02T06:54:33Z)
• "How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations [87.95711406978157]
  This work presents a new benchmark on spoken task-oriented conversations. We study multi-domain dialogue state tracking and knowledge-grounded dialogue modeling. Our data set enables speech-based benchmarking of task-oriented dialogue systems.
  arXiv  Detail & Related papers  (2021-09-28T04:51:04Z)
• DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization [19.918194137007653]
  We present a pre-training framework for long dialogue understanding and summarization. Considering the nature of long conversations, we propose a window-based denoising approach for generative pre-training. We conduct extensive experiments on five datasets of long dialogues, covering tasks of dialogue summarization, abstractive question answering and topic segmentation.
  arXiv  Detail & Related papers  (2021-09-06T13:55:03Z)
• Beyond Goldfish Memory: Long-Term Open-Domain Conversation [43.37382902468993]
  We release a human-human dataset consisting of multiple chat sessions. We show how existing models trained on existing datasets perform poorly in this long-term conversation setting. In particular, we find retrieval-augmented methods and methods with an ability to summarize and recall previous conversations.
  arXiv  Detail & Related papers  (2021-07-15T19:01:08Z)
• TIMEDIAL: Temporal Commonsense Reasoning in Dialog [43.24596551545824]
  We present the first study to investigate pre-trained language models for their temporal reasoning capabilities in dialogs. We formulate TIME-DIAL as a multiple-choice cloze task with over 1.1K carefully curated dialogs. Empirical results demonstrate that even the best performing models struggle on this task compared to humans.
  arXiv  Detail & Related papers  (2021-06-08T17:59:21Z)
• Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots [62.295373408415365]
  We propose a personalized hybrid matching network (PHMN) for context-response matching. Our contributions are two-fold: 1) our model extracts personalized wording behaviors from user-specific dialogue history as extra matching information. We evaluate our model on two large datasets with user identification, i.e., personalized dialogue Corpus Ubuntu (P- Ubuntu) and personalized Weibo dataset (P-Weibo)
  arXiv  Detail & Related papers  (2021-03-17T09:42:11Z)

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.