MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs

• URL: http://arxiv.org/abs/2403.05814v1
• Date: Sat, 9 Mar 2024 06:28:48 GMT
• Title: MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs
• Authors: Yerin Hwang, Yongil Kim, Yunah Jang, Jeesoo Bang, Hyunkyung Bae, Kyomin Jung
• Abstract summary: Multi-Passage to Dialogue (MP2D) generates question-answering datasets with natural topic transitions. MP2D maps the flow of topics within a dialogue, effectively mirroring the dynamics of human conversation. This study introduces a novel benchmark for topic shift dialogues, TS-WikiDialog.
• Score: 15.876075659237722
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite advancements in on-topic dialogue systems, effectively managing topic shifts within dialogues remains a persistent challenge, largely attributed to the limited availability of training datasets. To address this issue, we propose Multi-Passage to Dialogue (MP2D), a data generation framework that automatically creates conversational question-answering datasets with natural topic transitions. By leveraging the relationships between entities in a knowledge graph, MP2D maps the flow of topics within a dialogue, effectively mirroring the dynamics of human conversation. It retrieves relevant passages corresponding to the topics and transforms them into dialogues through the passage-to-dialogue method. Through quantitative and qualitative experiments, we demonstrate MP2D's efficacy in generating dialogue with natural topic shifts. Furthermore, this study introduces a novel benchmark for topic shift dialogues, TS-WikiDialog. Utilizing the dataset, we demonstrate that even Large Language Models (LLMs) struggle to handle topic shifts in dialogue effectively, and we showcase the performance improvements of models trained on datasets generated by MP2D across diverse topic shift dialogue tasks.

Related papers

• Unsupervised Extraction of Dialogue Policies from Conversations [3.102576158218633]
  We show how Large Language Models can be instrumental in extracting dialogue policies from datasets. We then propose a novel method for generating dialogue policies utilizing a controllable and interpretable graph-based methodology.
  arXiv  Detail & Related papers  (2024-06-21T14:57:25Z)
• 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)
• Multi-Granularity Prompts for Topic Shift Detection in Dialogue [13.739991183173494]
  The goal of dialogue topic shift detection is to identify whether the current topic in a conversation has changed or needs to change. Previous work focused on detecting topic shifts using pre-trained models to encode the utterance. We take a prompt-based approach to fully extract topic information from dialogues at multiple-granularity, i.e., label, turn, and topic.
  arXiv  Detail & Related papers  (2023-05-23T12:35:49Z)
• CTRLStruct: Dialogue Structure Learning for Open-Domain Response Generation [38.60073402817218]
  Well-structured topic flow can leverage background information and predict future topics to help generate controllable and explainable responses. We present a new framework for dialogue structure learning to effectively explore topic-level dialogue clusters as well as their transitions with unlabelled information. Experiments on two popular open-domain dialogue datasets show our model can generate more coherent responses compared to some excellent dialogue models.
  arXiv  Detail & Related papers  (2023-03-02T09:27:11Z)
• Manual-Guided Dialogue for Flexible Conversational Agents [84.46598430403886]
  How to build and use dialogue data efficiently, and how to deploy models in different domains at scale can be critical issues in building a task-oriented dialogue system. We propose a novel manual-guided dialogue scheme, where the agent learns the tasks from both dialogue and manuals. Our proposed scheme reduces the dependence of dialogue models on fine-grained domain ontology, and makes them more flexible to adapt to various domains.
  arXiv  Detail & Related papers  (2022-08-16T08:21:12Z)
• HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data [87.67278915655712]
  We present a new dialogue dataset, HybriDialogue, which consists of crowdsourced natural conversations grounded on both Wikipedia text and tables. The conversations are created through the decomposition of complex multihop questions into simple, realistic multiturn dialogue interactions.
  arXiv  Detail & Related papers  (2022-04-28T00:52:16Z)
• Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling [80.51094098799736]
  We propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder. BiDeN explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks. Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN.
  arXiv  Detail & Related papers  (2022-04-18T03:51:46Z)
• TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue Modeling on Spoken Conversations [24.245354500835465]
  We propose a novel model-agnostic data augmentation paradigm to boost the robustness of task-oriented dialogue modeling on spoken conversations. Our approach ranked first in both tasks of DSTC10 Track2, a benchmark for task-oriented dialogue modeling on spoken conversations.
  arXiv  Detail & Related papers  (2021-12-23T10:04:25Z)
• TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling [32.28415754809567]
  TIAGE is a new topic-shift aware dialog benchmark constructed utilizing human annotations on topic shifts. Based on TIAGE, we introduce three tasks to investigate different scenarios of topic-shift modeling in dialog settings.
  arXiv  Detail & Related papers  (2021-09-09T21:06:12Z)
• Response Selection for Multi-Party Conversations with Dynamic Topic Tracking [63.15158355071206]
  We frame response selection as a dynamic topic tracking task to match the topic between the response and relevant conversation context. We propose a novel multi-task learning framework that supports efficient encoding through large pretrained models. Experimental results on the DSTC-8 Ubuntu IRC dataset show state-of-the-art results in response selection and topic disentanglement tasks.
  arXiv  Detail & Related papers  (2020-10-15T14:21:38Z)
• Topic-Aware Multi-turn Dialogue Modeling [91.52820664879432]
  This paper presents a novel solution for multi-turn dialogue modeling, which segments and extracts topic-aware utterances in an unsupervised way. Our topic-aware modeling is implemented by a newly proposed unsupervised topic-aware segmentation algorithm and Topic-Aware Dual-attention Matching (TADAM) Network.
  arXiv  Detail & Related papers  (2020-09-26T08:43:06Z)

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.