End-to-End Reinforcement Learning of Dialogue Agents for Information Access

By Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung (Vivian) Chen, Faisal Ahmed, Li Deng. 2016

45 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jianfeng Gao
2. Mohit Bansal
3. Percy Liang
4. Caiming Xiong
5. Yoshua Bengio
6. Richard Socher
7. Zhe Gan
8. Bing Liu

Citations in the corpus

• Emergence of Grounded Compositional Language in Multi-Agent Populations (2017, background): “…years have seen substantial progress in practical natural language applications such as machine translation (Sutskever et al., 2014; Bahdanau et al., 2014), sentiment analysis (Socher et al., 2013), document summarization (Durrett et al., 2016), and domain-specific dialogue (Dhingra et al., 2016).”
• A Simple, Fast Diverse Decoding Algorithm for Neural Generation (2016, methodology): “The proposed RL based algorithm is inspired by a variety of recent reinforcement learning approaches in NLP for tasks such as dialogue (Dhingra et al., 2016), word compositions (Yogatama et al., 2016), machine translation (Ranzato et al., 2015), neural model visualization (Lei et al., 2016), and…”
• Key-Value Retrieval Networks for Task-Oriented Dialogue (2017, methodology): “…agents present one of the most promising avenues for leveraging dialogue corpora to build statistical models directly from data by using powerful distributed representations (Bordes and Weston, 2016; Wen et al., 2016b; Dhingra et al., 2016). no problem Figure 1: Sample dialogue from our dataset.”
• Domain Adaptive Dialog Generation via Meta Learning (2019, background): “, dialog state tracker (Henderson et al., 2014), dialog policy learning (Cuay´ahuitl et al., 2015; Young et al., 2013) and natural language generation (NLG) (Dhingra et al., 2017; Wen et al., 2015).”
• Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning (2017, background): “Recent advances in deep learning have inspired many deep reinforcement learning based dialogue systems that eliminate the need for feature engineering (Su et al., 2016; Cuay´ahuitl, 2017; Williams et al., 2017; Dhingra et al., 2017; Li et al., 2017a).”
• Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability (2017, methodology): “Another approach uses RNNs to automatically learn a distributed vector representation of the dialog state by accumulating the observations at each turn (Williams and Zweig, 2016; Zhao and Eskenazi, 2016; Dhingra et al., 2016; Williams et al., 2017).”
• Decoupling Strategy and Generation in Negotiation Dialogues (2018, methodology): “While end-to-end neural models have made promising progress in dialogue systems (Wen et al., 2017a; Dhingra et al., 2017), we find they # of unique postings 1402 % with images 80.”
• Sentiment Adaptive End-to-End Dialog Systems (2018, background): “Recently, more and more work combines all the modules in an end-to-end training framework (Wen et al., 2016; Li et al., 2017; Dhingra et al., 2016; Williams et al., 2017; Liu and Lane, 2017a).”
• A Teacher-Student Framework for Maintainable Dialog Manager (2018, background): “, 2017; Zhao and Eskenazi, 2016) and policy gradient methods (Williams et al., 2017; Su et al., 2016a; Dhingra et al., 2016).”
• Interactive reinforcement learning for task-oriented dialogue management (2016, background): “Using reinforcement learning based approaches to train a dialogue manager has been extensively researched (Levin and Pieraccini (1997); Williams (2006); Gašić et al. (2012); Pietquin (2013); Cuayáhuitl (2016); Dhingra et al. (2016)).”
• CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (2019, background): “…Walker et al., 2002; Raux et al., 2005; Mrksic et al., 2015; Asri et al., 2017; Budzianowski et al., 2018) and modelling innovation (Artzi and Zettlemoyer, 2011; Henderson et al., 2013; Lee and Dernoncourt, 2016; hao Su et al., 2016; Dhingra et al., 2016; Li et al., 2016; Mrksic et al., 2017).”
• Deep Reinforcement Learning: An Overview (2017, unknown): “Some recent papers follow: Asri et al. (2016), Bordes and Weston (2016), Chen et al. (2016b), Dhingra et al. (2016), Fatemi et al. (2016), Li et al. (2016a), Lipton et al. (2016), Mesnil et al. (2015), Mo et al. (2016), Shah et al. (2016), Su et al. (2016a), Wen et al. (2015a), Williams and Zweig…”
• Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings (2017, background): “Current task-oriented dialogue systems (Young et al., 2013; Wen et al., 2017; Dhingra et al., 2017) require a pre-defined dialogue state (e.g., slots such as food type and price range for a restaurant searching task) and a fixed set of dialogue acts (e.g., request, inform).”
• Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning (2018, background): “Common examples of systems that use this action space representation include conversational agents such as personal assistants (Dhingra et al., 2016; Li et al., 2017; Su et al., 2016; Lipton et al., 2016b; Liu et al., 2017; Zhao and Eskenazi, 2016; Wu et al., 2016), travel planners (Peng et al.,…”