Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability

By Tiancheng Zhao, Allen Lu, Kyusong Lee, Maxine Eskénazi. 2017

89 citations recorded by the source corpus.

Generative encoder-decoder models offer great promise in developing domain-general dialog systems. However, they have mainly been applied to open-domain conversations. This paper presents a practical and novel framework for building task-oriented dialog systems based on encoder-decoder models. This framework enables encoder-decoder models to accomplish slot-value independent decision-making and interact with external databases. Moreover, this paper shows the flexibility of the proposed method by interleaving chatting capability with a slot-filling system for better out-of-domain recovery. The models were trained on both real-user data from a bus information system and human-human chat data. Results show that the proposed framework achieves good performance in both offline evaluation metrics and in task success rate with human users.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

A Simple Language Model for Task-Oriented Dialogue (2020, methodology): “Several works used both dialogue history and knowledge bases as input and optimized neural encoder-decoder models to generate or retrieve system responses without modular supervision [13, 62, 29, 54, 56].”
Neural Approaches to Conversational AI (2018, background): “(Zhao et al., 2017) proposed a task-oriented dialogue agent based on the encoder-decoder model with chatting capability.”
Toward Continual Learning for Conversational Agents (2017, background): “There have been prior studies that partly address the data-intensiveness problem outside the neural model (Wen et al., 2016b; Williams et al., 2017; Zhao et al., 2017; Eshghi et al., 2017; Wen et al., 2016a; Zhao et al., 2017).”
Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems (2018, methodology): “End-to-end task-oriented dialog systems train a single model directly on text transcripts of di-alogs (Wen et al., 2017; Serban et al., 2016; Williams et al., 2017; Zhao et al., 2017; Seo et al., 2017; Serban et al., 2017).”
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue (2019, methodology): “In order to reduce human effort and scale up between domains, end-to-end dialogue systems, which input plain text and directly output system responses, have shown promising results based on recurrent neural networks (Zhao et al., 2017; Lei et al., 2018) and memory networks (Sukhbaatar et al., 2015).”
Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context (2019, background): “2017) to end-to-end trainable dialog models (Zhao et al. 2017; Wen et al. 2017; Eric and Manning 2017).”
Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation (2019, background): “For example, robust task-oriented dialog systems are required for appropriately replying to user’s questions (Li et al., 2017) and responding for chit-chat style conversation (Zhao et al., 2017).”
Fast Domain Adaptation for Goal-Oriented Dialogue Using a Hybrid Generative-Retrieval Transformer (2020, background): “The sequence-to-sequence model [1] gained wide adoption in chat-oriented [2] as well as goal-oriented dialogue [3].”
Paraphrase Augmented Task-Oriented Dialog Generation (2020, background): “Speciﬁcally, with the progress on sequence-to-sequence (seq2seq) learning (Sutskever et al., 2014), neural generative models have achieved promising performance on dialog response generation (Zhao et al., 2017; Lei et al., 2018; Zhang et al., 2019).”
Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems (2020, methodology): “, 2013), end-to-end approaches recently have gained much attention (Zhao et al., 2017; Eric and Manning, 2017; Madotto et al., 2018), since they free the task-oriented dialog systems from the manually designed pipeline modules and can be automatically scaled up to new domains.”
Structured Fusion Networks for Dialog (2019, background): “Zhao et al. (2017) enables task-oriented systems to make slot-value-independent decisions and improves out-of-domain recovery through the use of entity indexing and delexicalization.”