A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation

• URL: http://arxiv.org/abs/2109.04096v1
• Date: Thu, 9 Sep 2021 08:32:02 GMT
• Title: A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation
• Authors: Shilei Liu, Xiaofeng Zhao, Bochao Li, Feiliang Ren, Longhui Zhang, Shujuan Yin
• Abstract summary: We propose a novel three-stage learning framework based on weakly supervised learning which benefits from large scale ungrounded dialogues and unstructured knowledge base. Our approach can outperform other state-of-the-art methods with less training data, and even in zero-resource scenario, our approach still performs well.
• Score: 0.9926500244448218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge. Nevertheless, it is laborious to construct such knowledge-grounded dialogues, and existing models usually perform poorly when transfer to new domains with limited training samples. Therefore, building a knowledge-grounded dialogue system under the low-resource setting is a still crucial issue. In this paper, we propose a novel three-stage learning framework based on weakly supervised learning which benefits from large scale ungrounded dialogues and unstructured knowledge base. To better cooperate with this framework, we devise a variant of Transformer with decoupled decoder which facilitates the disentangled learning of response generation and knowledge incorporation. Evaluation results on two benchmarks indicate that our approach can outperform other state-of-the-art methods with less training data, and even in zero-resource scenario, our approach still performs well.

Related papers

• Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive Learning [71.8876256714229]
  We propose an entity-based contrastive learning framework for improving the robustness of knowledge-grounded dialogue systems. Our method achieves new state-of-the-art performance in terms of automatic evaluation scores.
  arXiv  Detail & Related papers  (2024-01-09T05:16:52Z)
• Contextual Knowledge Learning For Dialogue Generation [13.671946960656467]
  We present a novel approach to context and knowledge weighting as an integral part of model training. We guide the model training through a Contextual Knowledge Learning process which involves Latent Vectors for context and knowledge.
  arXiv  Detail & Related papers  (2023-05-29T16:54:10Z)
• Knowledge-grounded Dialog State Tracking [12.585986197627477]
  We propose to perform dialog state tracking grounded on knowledge encoded externally. We query relevant knowledge of various forms based on the dialog context. We demonstrate superior performance of our proposed method over strong baselines.
  arXiv  Detail & Related papers  (2022-10-13T01:34:08Z)
• 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)
• Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation [78.85622982191522]
  Existing systems perform poorly on unseen topics due to limited topics covered in the training data. We present PLUG, a language model that homogenizes different knowledge sources to a unified knowledge representation. It can achieve comparable performance with state-of-the-art methods under a fully-supervised setting.
  arXiv  Detail & Related papers  (2021-12-15T07:11:02Z)
• Are Pre-trained Language Models Knowledgeable to Ground Open Domain Dialogues? [20.598241369838668]
  We study knowledge-grounded dialogue generation with pre-trained language models. We find that by fine-tuning with a few dialogues containing knowledge, the pre-trained language models can outperform the state-of-the-art model.
  arXiv  Detail & Related papers  (2020-11-19T08:22:49Z)
• Enhancing Dialogue Generation via Multi-Level Contrastive Learning [57.005432249952406]
  We propose a multi-level contrastive learning paradigm to model the fine-grained quality of the responses with respect to the query. A Rank-aware (RC) network is designed to construct the multi-level contrastive optimization objectives. We build a Knowledge Inference (KI) component to capture the keyword knowledge from the reference during training and exploit such information to encourage the generation of informative words.
  arXiv  Detail & Related papers  (2020-09-19T02:41:04Z)
• Zero-Resource Knowledge-Grounded Dialogue Generation [29.357221039484568]
  We propose representing the knowledge that bridges a context and a response and the way that the knowledge is expressed as latent variables. We show that our model can achieve comparable performance with state-of-the-art methods that rely on knowledge-grounded dialogues for training.
  arXiv  Detail & Related papers  (2020-08-29T05:48:32Z)
• Low-Resource Knowledge-Grounded Dialogue Generation [74.09352261943913]
  We consider knowledge-grounded dialogue generation under a natural assumption that only limited training examples are available. We devise a disentangled response decoder in order to isolate parameters that depend on knowledge-grounded dialogues from the entire generation model. With only 1/8 training data, our model can achieve the state-of-the-art performance and generalize well on out-of-domain knowledge.
  arXiv  Detail & Related papers  (2020-02-24T16:20:32Z)
• 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.