Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog

By Xiao Xu, Wanxiang Che, Yue Zhang, Ting-Wei Liu. 2020

96 citations recorded by the source corpus.

Recent studies have shown remarkable success in end-to-end task-oriented dialog system. However, most neural models rely on large training data, which are only available for a certain number of task domains, such as navigation and scheduling. This makes it difficult to scalable for a new domain with limited labeled data. However, there has been relatively little research on how to effectively use data from all domains to improve the performance of each domain and also unseen domains. To this end, we investigate methods that can make explicit use of domain knowledge and introduce a shared-private network to learn shared and specific knowledge. In addition, we propose a novel Dynamic Fusion Network (DF-Net) which automatically exploit the relevance between the target domain and each domain. Results show that our models outperforms existing methods on multi-domain dialogue, giving the state-of-the-art in the literature. Besides, with little training data, we show its transferability by outperforming prior best model by 13.9% on average.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Dialogue & Conversational AI

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Citations in the corpus

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022, methodology): “Ruiqi For the KVRET dataset, instead of the version used in our main tables, we re-run another more widely used pre-processed version (Madotto et al., 2018; Wu et al., 2019; Qin et al., 2020) on T5-base, T5-large and T5-3b.”
Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems (2020, methodology): “Following the data processing in Qin et al. (2020), we obtain 1,839/117/141 dialogs for training/validation/test respectively.”
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling (2021, background): “[6] Libo Qin, Xiao Xu, Wanxiang Che, Yue Zhang, and Ting Liu.”
Exploring Auxiliary Reasoning Tasks for Task-oriented Dialog Systems with Meta Cooperative Learning (2021, methodology): “Following the data processing in (Qin et al. 2020), there are 1,839/117/141 dialogs for training/validation/testing, belonging to restaurant, attraction, and hotel domains.”
Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems (2020, methodology): “Following Qin et al. (2020), we select only the dialogues with a single domain, which is more challenging since less data is available, and we leave the multiple domains per dialogue to future work.”
Recent advances in deep learning based dialogue systems: a systematic survey (2021, background): “Actually, there are quite a few recent works in dialogue systems that train GANs with reinforcement learning framework (Zhu et al., 2018; Wu et al., 2019b; He et al., 2020a; Zhu et al., 2020; Qin et al., 2020).”
Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems (2020, unknown): “As reported, compared with GLMP and DF-Net, COMET can generate (Wu et al., 2019) and DF-Net (Qin et al., 2020) from the SMD dataset.”