[PDF] Top 20 Adversarial Learning for Neural Dialogue Generation

... One caveat with the adversarial evaluation methods is that they are model-dependent. We approximate the human evaluator in the Turing test with an au- tomatic evaluator and assume that the evaluator is perfect: ... See full document

... conditional generation architectures and a novel method called snapshot learning to improve response generation in a neural dialogue system ...snapshot learning pro- vided gains ... See full document

... Wen et al. (2015) proposed a conditional language model (Semantically Conditioned Long Short-Term Memory; SC-LSTM) for task-oriented systems, which generates utterances on the basis of any dialogue acts and frames ... See full document

... 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

... (DAs). This task is particularly useful for dialogue systems, as knowing the DA of an utterance sup- ports its interpretation, and the generation of an appropriate response. The DA classification prob- lem ... See full document

... 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

... Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be short- sighted, predicting utterances one at a time while ignoring ... See full document

... the dialogue sys- tem with the knowledge base through both facts matching and entity diffusion, which enable the convergent and divergent thinking over the knowl- edge ...reinforcement learning and ... See full document

... We implemented our model with ParlAI (Miller et al., 2017). The sequence lengths are truncated at 50. We used Adam (Kingma and Ba, 2014) with an initial learning rate of 0.001 to optimize the model. For all the ... See full document

... recurrent neural networks to capture long-term context state within a dia- ...an adversarial dis- criminator in order to increase diversity and pro- vide a strong and calibrated guarantee to the gen- ... See full document

... ment learning problem (Li et ...by adversarial learning (Li et ...similar adversarial setting, Zhang et ...an adversarial model, the difference is that we employ the N-best response ... See full document

... multi-turn dialogue, the multi-turn ob- jective is only applied at inference and not used for actual model ...modeling dialogue response in multi-turn ...hand, adversarial system has been used for ... See full document

... open-domain dialogue systems, genera- tive approaches have attracted much attention for response 1 ...Dual Adversarial Learn- ing (DAL) for high-quality response genera- ...query generation and ... See full document

... Reinforcement Learning (RL) framework in which policy and NLG components can be jointly optimised and adapted based on on- line user ...active learning to miti- gate the data sparsity problem when training ... See full document

... As mentioned above, the maximum likelihood estimation technique utilized in RNN based architectures such as Seq2Seq often end up generating redundant and meaningless responses such as “I don’t know” or “Maybe” due to the ... See full document

... Controllable neural text generation Re- searchers have proposed several approaches to control aspects of RNN-based natural language generation such as sentiment, length, speaker style and tense (Fan ... See full document

... gether in an end-to-end manner becomes a more promising direction. Despite its importance, ex- isting studies are only exploring the performance in a single domain, while ignoring the transfer- ability across domains. To ... See full document

... research on data programming has produced a paradigm for weak supervision where data scientists write label- ing functions that create noisy labels (Ratner et al. 2017; 2016). The approach then discovers relationships ... See full document

... From the second block in Table 4, we can see that Multi-Task strategy performs clearly better than Single-Task. Note that, AMTN- Discriminator has an accuracy rate of 63.6% and 74.5% for RQE and QA tasks, which is the ... See full document

... deep learning models are able to produce accurate estimates of skeletal maturity as well as or even with higher accuracy than trained radiologist ...deep neural networks on chest X-ray photographs to detect ... See full document