CET2: Modelling Topic Transitions for Coherent and Engaging Knowledge-Grounded Conversations

• URL: http://arxiv.org/abs/2403.01848v1
• Date: Mon, 4 Mar 2024 08:55:34 GMT
• Title: CET2: Modelling Topic Transitions for Coherent and Engaging Knowledge-Grounded Conversations
• Authors: Lin Xu, Qixian Zhou, Jinlan Fu, See-Kiong Ng
• Abstract summary: Knowledge-grounded dialogue systems aim to generate coherent and engaging responses based on the dialogue contexts and selected external knowledge. Previous knowledge selection methods tend to rely too heavily on the dialogue contexts or over-emphasize the new information in the selected knowledge. We introduce a Coherent and Engaging Topic Transition framework to model topic transitions for selecting knowledge coherent to the context of the conversations.
• Score: 44.32118148085158
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Knowledge-grounded dialogue systems aim to generate coherent and engaging responses based on the dialogue contexts and selected external knowledge. Previous knowledge selection methods tend to rely too heavily on the dialogue contexts or over-emphasize the new information in the selected knowledge, resulting in the selection of repetitious or incongruous knowledge and further generating repetitive or incoherent responses, as the generation of the response depends on the chosen knowledge. To address these shortcomings, we introduce a Coherent and Engaging Topic Transition (CET2) framework to model topic transitions for selecting knowledge that is coherent to the context of the conversations while providing adequate knowledge diversity for topic development. Our CET2 framework considers multiple factors for knowledge selection, including valid transition logic from dialogue contexts to the following topics and systematic comparisons between available knowledge candidates. Extensive experiments on two public benchmarks demonstrate the superiority and the better generalization ability of CET2 on knowledge selection. This is due to our well-designed transition features and comparative knowledge selection strategy, which are more transferable to conversations about unseen topics. Analysis of fine-grained knowledge selection accuracy also shows that CET2 can better balance topic entailment (contextual coherence) and development (knowledge diversity) in dialogue than existing approaches.

Related papers

• FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems [53.89014188309486]
  We present a flexible neural framework that can integrate contextual information from multiple channels. We evaluate our model on the MSDialog dataset widely used for evaluating conversational response ranking tasks.
  arXiv  Detail & Related papers  (2023-03-31T23:58:28Z)
• Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access [20.881612071473118]
  We propose Topic-Aware Response Generation'' (TARG) to better integrate topical information in task-oriented dialogue. TARG incorporates multiple topic-aware attention mechanisms to derive the importance weighting scheme over dialogue utterances and external knowledge sources.
  arXiv  Detail & Related papers  (2022-12-10T22:32:28Z)
• RT-KGD: Relation Transition Aware Knowledge-Grounded Dialogue Generation [20.37399983466163]
  We propose a Relation Transition aware Knowledge-Grounded Dialogue Generation model (RT-KGD) Specifically, inspired by the latent logic of human conversation, our model integrates dialogue-level relation transition regularities with turn-level entity semantic information. In this manner, the interaction between knowledge is considered to produce abundant clues for predicting the appropriate knowledge and generating coherent responses.
  arXiv  Detail & Related papers  (2022-07-17T16:07:38Z)
• Multimodal Dialog Systems with Dual Knowledge-enhanced Generative Pretrained Language Model [63.461030694700014]
  We propose a novel dual knowledge-enhanced generative pretrained language model for multimodal task-oriented dialog systems (DKMD) The proposed DKMD consists of three key components: dual knowledge selection, dual knowledge-enhanced context learning, and knowledge-enhanced response generation. Experiments on a public dataset verify the superiority of the proposed DKMD over state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-07-16T13:02:54Z)
• Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge [77.77684299758494]
  We extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG) First, we present a context-knowledge interaction learning framework, Knowledge-aware Neural Network (KNN), which learns the QA sentence representations by considering a tight interaction with the external knowledge from KG and the textual information. To handle the diversity and complexity of KG information, we propose a Contextualized Knowledge-aware Attentive Neural Network (CKANN), which improves the knowledge representation learning with structure information via a customized Graph Convolutional Network (GCN) and comprehensively learns context-based and knowledge-based sentence representation via
  arXiv  Detail & Related papers  (2021-04-12T05:52:20Z)
• Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge [43.352833140317486]
  Multi-turn dialogue reading comprehension aims to teach machines to read dialogue contexts and solve tasks such as response selection and answering questions. This work makes the first attempt to tackle the above two challenges by extracting substantially important turns as pivot utterances. We propose a pivot-oriented deep selection model (PoDS) on top of the Transformer-based language models for dialogue comprehension.
  arXiv  Detail & Related papers  (2021-02-10T15:00:12Z)
• Knowledgeable Dialogue Reading Comprehension on Key Turns [84.1784903043884]
  Multi-choice machine reading comprehension (MRC) requires models to choose the correct answer from candidate options given a passage and a question. Our research focuses dialogue-based MRC, where the passages are multi-turn dialogues. It suffers from two challenges, the answer selection decision is made without support of latently helpful commonsense, and the multi-turn context may hide considerable irrelevant information.
  arXiv  Detail & Related papers  (2020-04-29T07:04:43Z)
• Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue [51.513276162736844]
  We propose a sequential latent variable model as the first approach to this matter. The model named sequential knowledge transformer (SKT) can keep track of the prior and posterior distribution over knowledge.
  arXiv  Detail & Related papers  (2020-02-18T11:59:59Z)

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.