Hybrid Supervised Reinforced Model for Dialogue Systems

• URL: http://arxiv.org/abs/2011.02243v1
• Date: Wed, 4 Nov 2020 12:03:12 GMT
• Title: Hybrid Supervised Reinforced Model for Dialogue Systems
• Authors: Carlos Miranda and Yacine Kessaci
• Abstract summary: The model copes with both tasks required for Dialogue Management: State Tracking and Decision Making. The model achieves greater performance, learning speed and robustness than a non-recurrent baseline.
• Score: 2.1485350418225244
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents a recurrent hybrid model and training procedure for task-oriented dialogue systems based on Deep Recurrent Q-Networks (DRQN). The model copes with both tasks required for Dialogue Management: State Tracking and Decision Making. It is based on modeling Human-Machine interaction into a latent representation embedding an interaction context to guide the discussion. The model achieves greater performance, learning speed and robustness than a non-recurrent baseline. Moreover, results allow interpreting and validating the policy evolution and the latent representations information-wise.

Related papers

• SAIE Framework: Support Alone Isn't Enough -- Advancing LLM Training with Adversarial Remarks [47.609417223514605]
  This work introduces the SAIE framework, which facilitates supportive and adversarial discussions between learner and partner models. Our empirical evaluation shows that models fine-tuned with the SAIE framework outperform those trained with conventional fine-tuning approaches.
  arXiv  Detail & Related papers  (2023-11-14T12:12:25Z)
• Relational Temporal Graph Reasoning for Dual-task Dialogue Language Understanding [39.76268402567324]
  Dual-task dialog understanding language aims to tackle two correlative dialog language understanding tasks simultaneously via their inherent correlations. We put forward a new framework, whose core is relational temporal graph reasoning. Our models outperform state-of-the-art models by a large margin.
  arXiv  Detail & Related papers  (2023-06-15T13:19:08Z)
• Improving a sequence-to-sequence nlp model using a reinforcement learning policy algorithm [0.0]
  Current neural network models of dialogue generation show great promise for generating answers for chatty agents. But they are short-sighted in that they predict utterances one at a time while disregarding their impact on future outcomes. This work commemorates a preliminary step toward developing a neural conversational model based on the long-term success of dialogues.
  arXiv  Detail & Related papers  (2022-12-28T22:46:57Z)
• Learning Interpretable Latent Dialogue Actions With Less Supervision [3.42658286826597]
  We present a novel architecture for explainable modeling of task-oriented dialogues with discrete latent variables. Our model is based on variational recurrent neural networks (VRNN) and requires no explicit annotation of semantic information.
  arXiv  Detail & Related papers  (2022-09-22T16:14:06Z)
• 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)
• 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)
• 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)
• Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System [49.39150449455407]
  HDNO is an option framework for designing latent dialogue acts to avoid designing specific dialogue act representations. We test HDNO on MultiWoz 2.0 and MultiWoz 2.1, the datasets on multi-domain dialogues, in comparison with word-level E2E model trained with RL, LaRL and HDSA.
  arXiv  Detail & Related papers  (2020-06-11T20:55:28Z)
• Modeling Long Context for Task-Oriented Dialogue State Generation [51.044300192906995]
  We propose a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model. Our approaches attempt to solve the problem that the performance of the baseline significantly drops when the input dialogue context sequence is long. In our experiments, our proposed model achieves a 7.03% relative improvement over the baseline, establishing a new state-of-the-art joint goal accuracy of 52.04% on the MultiWOZ 2.0 dataset.
  arXiv  Detail & Related papers  (2020-04-29T11:02:25Z)

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.