Enhancing Goal-oriented Proactive Dialogue Systems via Consistency Reflection and Correction

• URL: http://arxiv.org/abs/2506.13366v3
• Date: Wed, 18 Jun 2025 06:17:35 GMT
• Title: Enhancing Goal-oriented Proactive Dialogue Systems via Consistency Reflection and Correction
• Authors: Didi Zhang, Yaxin Fan, Peifeng Li, Qiaoming Zhu,
• Abstract summary: We introduce a model-agnostic two-stage Consistency Reflection and Correction framework.<n>In the consistency reflection stage, the model is prompted to reflect on the discrepancies between generated responses and dialogue contexts.<n>In the consistency correction stage, the model generates responses that are more consistent with the dialogue context.
• Score: 14.520176577205754
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Goal-oriented proactive dialogue systems are designed to guide user conversations seamlessly towards specific objectives by planning a goal-oriented path. However, previous research has focused predominantly on optimizing these paths while neglecting the inconsistencies that may arise between generated responses and dialogue contexts, including user profiles, dialogue history, domain knowledge, and subgoals. To address this issue, we introduce a model-agnostic two-stage Consistency Reflection and Correction (CRC) framework. Specifically, in the consistency reflection stage, the model is prompted to reflect on the discrepancies between generated responses and dialogue contexts, identifying inconsistencies and suggesting possible corrections. In the consistency correction stage, the model generates responses that are more consistent with the dialogue context based on these reflection results. We conducted experiments on various model architectures with different parameter sizes, including encoder-decoder models (BART, T5) and decoder-only models (GPT-2, DialoGPT, Phi3, Mistral and LLaMA3), and the experimental results on three datasets demonstrate that our CRC framework significantly improves the consistency between generated responses and dialogue contexts.

Related papers

• Language-guided Image Reflection Separation [48.06512741731805]
  We propose a unified framework to solve this problem. A gated network design and a randomized training strategy are employed to tackle the recognizable layer ambiguity.
  arXiv  Detail & Related papers  (2024-02-19T06:32:23Z)
• Learning Locality and Isotropy in Dialogue Modeling [28.743212772593335]
  We propose a simple method for dialogue representation calibration, namely SimDRC, to build isotropic and conversational feature spaces. Experimental results show that our approach significantly outperforms the current state-of-the-art models on three dialogue tasks.
  arXiv  Detail & Related papers  (2022-05-29T06:48:53Z)
• Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation [32.764356638437214]
  We introduce a new technique for target-guided response generation. We also propose techniques to re-purpose existing dialogue datasets for target-guided generation. Our work generally enables dialogue system designers to exercise more control over the conversations that their systems produce.
  arXiv  Detail & Related papers  (2022-05-19T04:01:40Z)
• DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings [33.89889949577356]
  We propose DialogueCSE, a dialogue-based contrastive learning approach to tackle this issue. We evaluate our model on three multi-turn dialogue datasets: the Microsoft Dialogue Corpus, the Jing Dong Dialogue Corpus, and the E-commerce Dialogue Corpus.
  arXiv  Detail & Related papers  (2021-09-26T13:25:41Z)
• Structural Pre-training for Dialogue Comprehension [51.215629336320305]
  We present SPIDER, Structural Pre-traIned DialoguE Reader, to capture dialogue exclusive features. To simulate the dialogue-like features, we propose two training objectives in addition to the original LM objectives. Experimental results on widely used dialogue benchmarks verify the effectiveness of the newly introduced self-supervised tasks.
  arXiv  Detail & Related papers  (2021-05-23T15:16:54Z)
• CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues [14.66729951223073]
  Anaphora and ellipses are two common phenomena in dialogues. Traditionally, anaphora is resolved by coreference resolution and ellipses by query rewrite. We propose a novel joint learning framework of modeling coreference resolution and query rewriting.
  arXiv  Detail & Related papers  (2021-05-20T17:17:26Z)
• Dialogue Response Selection with Hierarchical Curriculum Learning [52.3318584971562]
  We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that random negatives are often too trivial to train a reliable model, we propose a hierarchical curriculum learning framework.
  arXiv  Detail & Related papers  (2020-12-29T14:06:41Z)
• I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling [104.09033240889106]
  We introduce the DialoguE COntradiction DEtection task (DECODE) and a new conversational dataset containing both human-human and human-bot contradictory dialogues. We then compare a structured utterance-based approach of using pre-trained Transformer models for contradiction detection with the typical unstructured approach.
  arXiv  Detail & Related papers  (2020-12-24T18:47:49Z)
• 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)
• Controlling Dialogue Generation with Semantic Exemplars [55.460082747572734]
  We present an Exemplar-based Dialogue Generation model, EDGE, that uses the semantic frames present in exemplar responses to guide generation. We show that controlling dialogue generation based on the semantic frames of exemplars, rather than words in the exemplar itself, improves the coherence of generated responses.
  arXiv  Detail & Related papers  (2020-08-20T17:02:37Z)
• DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification [77.59549450705384]
  In dialog system, dialog act recognition and sentiment classification are two correlative tasks. Most of the existing systems either treat them as separate tasks or just jointly model the two tasks. We propose a Deep Co-Interactive Relation Network (DCR-Net) to explicitly consider the cross-impact and model the interaction between the two tasks.
  arXiv  Detail & Related papers  (2020-08-16T14:13:32Z)
• Dialogue-Based Relation Extraction [53.2896545819799]
  We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings.
  arXiv  Detail & Related papers  (2020-04-17T03:51:57Z)
• Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation [59.174903564894954]
  In this work, we extend this approach to the task of dialog state tracking for goal-oriented dialogs. We propose the Variational Hierarchical Dialog Autoencoder (VHDA) for modeling the complete aspects of goal-oriented dialogs. Experiments on various dialog datasets show that our model improves the downstream dialog trackers' robustness via generative data augmentation.
  arXiv  Detail & Related papers  (2020-01-23T15:34:56Z)

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.