[PDF] Top 20 DAL: Dual Adversarial Learning for Dialogue Generation
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DAL: Dual Adversarial Learning for Dialogue Generation
... Generative Adversarial Networks (GAN), has proven to be a promising approach for generation ...sequence generation as an action-taking problem in reinforcement learning, Li et ...to ... See full document
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Goal Embedded Dual Hierarchical Model for Task Oriented Dialogue Generation
... N-gram models have been used before (Goodman, 2001; Katz, 1987; Kneser and Ney, 1995). Re- cently, RNN-based models have achieved a better performance (Mikolov et al., 2010; J´ozefowicz et al., 2016; Grave et al., 2017; ... See full document
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Deep Adversarial Learning for NLP
... deep learning extensions such as Generative Adversarial Networks (Goodfellow et ...deep adversarial learning in NLP listed ...on generation of adversarial exam- ples and their ... See full document
5
Neural Conversation Model Controllable by Given Dialogue Act Based on Adversarial Learning and Label aware Objective
... response generation given a dialogue act label and its existing approaches (Section ...an adversarial learning frame- work and extend its architecture and objective to fit the problem of ... See full document
10
Dual Supervised Learning for Natural Language Understanding and Generation
... a dialogue state tracker (DST) that pre- dicts the current dialogue state in the multi-turn conversations, 4) a dialogue policy that determines the system action for the next step given the cur- rent ... See full document
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Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning
... of adversarial dialogue generation models relies on the quality of the reward signal produced by the ...proposed adversarial dialogue generation method to an adversarial ... See full document
8
Dual Latent Variable Model for Low Resource Natural Language Generation in Dialogue Systems
... In this context, one can think of a potential so- lution where the domain adaptation learning is uti- lized. The source domain, in this scenario, typ- ically contains a sufficient amount of annotated data such ... See full document
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Adversarial evaluation for open domain dialogue generation
... End-to-end dialogue response generation systems trained to produce a plausible utterance given some limited dialogue context are receiving in- creased attention (Vinyals and Le, 2015; Sordoni et ... See full document
5
Multi turn Dialogue Response Generation in an Adversarial Learning Framework
... domain dialogue models. Most neural dialogue models use transduction frame- works adapted from neural machine translation (Sutskever et ...ment learning framework (Yu et ...with adversarial ... See full document
12
Adversarial Learning for Neural Dialogue Generation
... handwriting generation. Yu et al. (2016a) use policy gradient reinforcement learning to backpropagate the error from the discriminator, showing improvement in multiple generation tasks such as poem ... See full document
13
Learning to Adapt to Unknown Users: Referring Expression Generation in Spoken Dialogue Systems
... in learning mode us- ing the above reward function using the SHAR- SHA reinforcement learning algorithm (with lin- ear function approximation) (Shapiro and Langley, ...on-policy learning algorithm ... See full document
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Joint Learning of a Dual SMT System for Paraphrase Generation
... joint learning method for pivot language-based paraphrase ...learned dual SMT system which combines the train- ing processes of two SMT systems in paraphrase generation, enables optimization of the ... See full document
5
Few Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach
... in dialogue settings — see examples in Table 2 of the Appendix (see also Novikova et ...in dialogue model eval- uation since the variability of possible responses equivalent in meaning is very high in ... See full document
8
ARAML: A Stable Adversarial Training Framework for Text Generation
... Dialogue evaluation is an open problem and ex- isting works have found that automatic metrics have low correlation to human evaluation (Liu et al., 2016; Novikova et al., 2017; Chaganty et al., 2018). Thus, we ... See full document
11
Multi task Learning for Natural Language Generation in Task Oriented Dialogue
... deep learning technology in natural language processing increases these models’ ca- pacity to generate sophisticated human-like re- ...task-oriented dialogue systems to generate truly natural utterance ... See full document
6
Towards a Professional Learning Dialogue in Mexican Contemporary Art Museums
... This perspective demonstrates that some interventions can ease audiences’ understanding and access to contemporary art. Bourdieu and Darbel’s view may not appreciate that some artworks can be closely related to ... See full document
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Dialogue Strategy Learning in Healthcare: A Systematic Approach for Learning Dialogue Models from Data
... Reinforcement learning (RL): RL in (partially observable) Markov decision processes, so called the (PO)MDPs, is a learning approach in sequen- tial decision ...in dialogue agents (Roy et ... See full document
7
Unsupervised Dialogue Spectrum Generation for Log Dialogue Ranking
... Task-oriented dialogue systems provide a natu- ral interface to accomplish various daily-life tasks such as restaurant finding and flight ...create dialogue systems with a limited amount of data in their ... See full document
12
Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email
... Learning Optimal Dialogue Strategies A Case Study of a Spoken Dialogue Agent for Email Learning Optimal Dialogue Strategies A Case Study of a Spoken Dialogue Agent for Email Marilyn A Walker walker @[.] ... See full document
7
Adversarial Over Sensitivity and Over Stability Strategies for Dialogue Models
... on adversarial training and Generative ...each adversarial strategy (as well as on all Should- Not-Change strategies combined) indeed on aver- age produced better responses, and mostly agrees with the ... See full document
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