Dept. of Computer Science, Ramakrishna Mission Vivekananda Educational and Research Institute, Belur, Howrah, India.
Dept. of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, Bihar, India.
PLoS One. 2023 Jan 5;18(1):e0275750. doi: 10.1371/journal.pone.0275750. eCollection 2023.
Existing task-oriented virtual agents can assist users with simple tasks like ticket booking, hotel reservations, etc. effectively and with high confidence. These virtual assistants, however, assume specific, predictable end-user behavior, such as predefined/servable objectives, which results in conversation failures in challenging situations, such as when goals are unavailable.
Inspired by the practice and its efficacy, we propose an end-to-end framework for task-oriented persuasive dialogue generation that combines pre-training and reinforcement learning for generating context-aware persuasive responses. We utilize four novel rewards to improve consistency and repetitiveness in generated responses. Additionally, a meta-learning strategy has also been utilized to make the model parameters better for domain adaptation. Furthermore, we also curate a personalized persuasive dialogue (PPD) corpus, which contains utterance-level intent, slot, sentiment, and persuasion strategy annotation.
The obtained results and detailed analysis firmly establish the effectiveness of the proposed persuasive virtual assistant over traditional task-oriented virtual assistants. The proposed framework considerably increases the quality of dialogue generation in terms of consistency and repetitiveness. Additionally, our experiment with a few shot and zero-shot settings proves that our meta-learned model learns to quickly adopt new domains with a few or even zero no. of training epochs. It outperforms the non-meta-learning-based approaches keeping the base model constant.
To the best of our knowledge, this is the first effort to improve a task-oriented virtual agent's persuasiveness and domain adaptation.
现有的面向任务的虚拟助手可以有效地帮助用户完成简单的任务，例如订票、预订酒店等，并且具有很高的信心。然而，这些虚拟助手假设特定的、可预测的最终用户行为，例如预定义/可服务的目标，这导致在具有挑战性的情况下（例如目标不可用时）会出现对话失败。
受实践及其功效的启发，我们提出了一种端到端的面向任务的有说服力的对话生成框架，该框架结合了预训练和强化学习，用于生成上下文感知的有说服力的响应。我们利用四个新颖的奖励来提高生成响应的一致性和重复性。此外，还利用元学习策略使模型参数更适合领域自适应。此外，我们还精心制作了一个个性化的有说服力的对话（PPD）语料库，其中包含话语级意图、插槽、情感和说服策略注释。
所获得的结果和详细分析坚定地确立了所提出的有说服力的虚拟助手相对于传统面向任务的虚拟助手的有效性。所提出的框架在一致性和重复性方面大大提高了对话生成的质量。此外，我们在少量和零样本设置下的实验证明，我们的元学习模型可以快速学习采用新的领域，只需几个甚至零个训练时期。它优于保持基础模型不变的非元学习方法。
据我们所知，这是首次努力提高面向任务的虚拟代理的说服力和领域适应性。
