Deep learning models are often not easily adaptable to new tasks and require task-specific adjustments. The differentiable neural computer (DNC), a memory-augmented neural network, is designed as a general problem solver which can be used in a wide range of tasks. But in reality, it is hard to apply this model to new tasks. We analyze the DNC and identify possible improvements within the application of question answering. This motivates a more robust and scalable DNC (rsDNC). The objective precondition is to keep the general character of this model intact while making its application more reliable and speeding up its required training time. The rsDNC is distinguished by a more robust training, a slim memory unit and a bidirectional architecture. We not only achieve new state-of-the-art performance on the bAbI task, but also minimize the performance variance between different initializations. Furthermore, we demonstrate the simplified applicability of the rsDNC to new tasks with passable results on the CNN RC task without adaptions.
provides technical enhancements with the introduction of the sparse DNC (SDNC) with sparse read and write operations. They allow to modifying only a sparse subset of interesting memory locations instead of manipulating all. This renders the memory consumption independent of memory size. A different approach provides the dynamic memory network (DMN) and its successor the DMN+ @cite_11 @cite_4 . They store sentence representations in an episodic memory and use attention to find the correct answer.
@cite_4 @cite_11
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We propose a novel memory network model named Read-Write Memory Network (RWMN) to perform question and answering tasks for large-scale, multimodal movie story understanding. The key focus of our RWMN model is to design the read network and the write network that consist of multiple convolutional layers, which enable memory read and write operations to have high capacity and flexibility. While existing memory-augmented network models treat each memory slot as an independent block, our use of multi-layered CNNs allows the model to read and write sequential memory cells as chunks, which is more reasonable to represent a sequential story because adjacent memory blocks often have strong correlations. For evaluation, we apply our model to all the six tasks of the MovieQA benchmark [24], and achieve the best accuracies on several tasks, especially on the visual QA task. Our model shows a potential to better understand not only the content in the story, but also more abstract information, such as relationships between characters and the reasons for their actions. In this paper, we present an improved feedforward sequential memory networks (FSMN) architecture, namely Deep-FSMN (DFSMN), by introducing skip connections between memory blocks in adjacent layers. These skip connections enable the information flow across different layers and thus alleviate the gradient vanishing problem when building very deep structure. As a result, DFSMN significantly benefits from these skip connections and deep structure. We have compared the performance of DFSMN to BLSTM both with and without lower frame rate (LFR) on several large speech recognition tasks, including English and Mandarin. Experimental results shown that DFSMN can consistently outperform BLSTM with dramatic gain, especially trained with LFR using CD-Phone as modeling units. In the 20000 hours Fisher (FSH) task, the proposed DFSMN can achieve a word error rate of 9.4 by purely using the cross-entropy criterion and decoding with a 3-gram language model, which achieves a 1.5 absolute improvement compared to the BLSTM. In a 20000 hours Mandarin recognition task, the LFR trained DFSMN can achieve more than 20 relative improvement compared to the LFR trained BLSTM. Moreover, we can easily design the lookahead filter order of the memory blocks in DFSMN to control the latency for real-time applications. This paper proposes Dyadic Memory Networks (DyMemNN), a novel extension of end-to-end memory networks (memNN) for aspect-based sentiment analysis (ABSA). Originally designed for question answering tasks, memNN operates via a memory selection operation in which relevant memory pieces are adaptively selected based on the input query. In the problem of ABSA, this is analogous to aspects and documents in which the relationship between each word in the document is compared with the aspect vector. In the standard memory networks, simple dot products or feed forward neural networks are used to model the relationship between aspect and words which lacks representation learning capability. As such, our dyadic memory networks ameliorates this weakness by enabling rich dyadic interactions between aspect and word embeddings by integrating either parameterized neural tensor compositions or holographic compositions into the memory selection operation. To this end, we propose two variations of our dyadic memory networks, namely the Tensor DyMemNN and Holo DyMemNN. Overall, our two models are end-to-end neural architectures that enable rich dyadic interaction between aspect and document which intuitively leads to better performance. Via extensive experiments, we show that our proposed models achieve the state-of-the-art performance and outperform many neural architectures across six benchmark datasets. Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering. One such architecture, the dynamic memory network (DMN), obtained high accuracy on a variety of language tasks. However, it was not shown whether the architecture achieves strong results for question answering when supporting facts are not marked during training or whether it could be applied to other modalities such as images. Based on an analysis of the DMN, we propose several improvements to its memory and input modules. Together with these changes we introduce a novel input module for images in order to be able to answer visual questions. Our new DMN+ model improves the state of the art on both the Visual Question Answering dataset and the -10k text question-answering dataset without supporting fact supervision.