Ever since the successful application of sequence to sequence learning for neural machine translation systems, interest has surged in its applicability towards language generation in other problem domains. Recent work has investigated the use of these neural architectures towards modeling open-domain conversational dialogue, where it has been found that although these models are capable of learning a good distributional language model, dialogue coherence is still of concern. Unlike translation, conversation is much more a one-to-many mapping from utterance to a response, and it is even more pressing that the model be aware of the preceding flow of conversation. In this paper we propose to tackle this problem by introducing previous conversational context in terms of latent representations of dialogue acts over time. We inject the latent context representations into a sequence to sequence neural network in the form of dialog acts using a second encoder to enhance the quality and the coherence of the conversations generated. The main task of this research work is to show that adding latent variables that capture discourse relations does indeed result in more coherent responses when compared to conventional sequence to sequence models.
@cite_10 propose a context topic sensitive question answering system where a convolution neural network is pre-trained to predict one among 40 topics. The convolutional model is given as input a window of the previous conversation and the hidden state of the encoder before the softmax layer is fed as input(context) to a sequence to sequence model along with the current utterance for coherent answer generation.
@cite_10
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Given a convolutional neural network (CNN) that is pre-trained for object classification, this paper proposes to use active question-answering to semanticize neural patterns in conv-layers of the CNN and mine part concepts. For each part concept, we mine neural patterns in the pre-trained CNN, which are related to the target part, and use these patterns to construct an And-Or graph (AOG) to represent a four-layer semantic hierarchy of the part. As an interpretable model, the AOG associates different CNN units with different explicit object parts. We use an active human-computer communication to incrementally grow such an AOG on the pre-trained CNN as follows. We allow the computer to actively identify objects, whose neural patterns cannot be explained by the current AOG. Then, the computer asks human about the unexplained objects, and uses the answers to automatically discover certain CNN patterns corresponding to the missing knowledge. We incrementally grow the AOG to encode new knowledge discovered during the active-learning process. In experiments, our method exhibits high learning efficiency. Our method uses about 1 6&#x2013;1 3 of the part annotations for training, but achieves similar or better part-localization performance than fast-RCNN methods.