A sequence to sequence model for dialogue generation with gated mixture of topics

[1]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[2]  Diana Pérez-Marín,et al.  Conversational Agents and Natural Language Interaction : Techniques and Effective Practices , 2011 .

[3]  William C. Mann,et al.  Rhetorical Structure Theory: Toward a functional theory of text organization , 1988 .

[4]  Arthur C. Graesser,et al.  AutoTutor: an intelligent tutoring system with mixed-initiative dialogue , 2005, IEEE Transactions on Education.

[5]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[6]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[7]  Yoshua Bengio,et al.  Practical Recommendations for Gradient-Based Training of Deep Architectures , 2012, Neural Networks: Tricks of the Trade.

[8]  Helen F. Hastie,et al.  A survey on metrics for the evaluation of user simulations , 2012, The Knowledge Engineering Review.

[9]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[10]  Wei-Ying Ma,et al.  Topic Aware Neural Response Generation , 2016, AAAI.

[11]  Joelle Pineau,et al.  Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models , 2015, AAAI.

[12]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[13]  Timothy Bickmore,et al.  Health dialog systems for patients and consumers , 2006, J. Biomed. Informatics.

[14]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[15]  Yang Liu,et al.  Context Gates for Neural Machine Translation , 2016, TACL.