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, 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.

我们提出了一种名为 Read-Write 记忆网络 (RWMN) 的新型记忆网络模型，用于大规模、多模态电影故事理解的问答任务。我们的 RWMN 模型的重点是设计读取网络和写入网络，由多个卷积层组成，从而使内存读取和写入操作具有高容量和灵活性。采用多层 CNN 的读写方式，更合理地表示顺序故事，从而实现了顺序存储的表达。我们应用模型到 MovieQA 基准测试的六个任务中，取得了最好的正确率，尤其是在视觉问答任务上。我们的模型表现出更好地理解故事中的内容，以及角色之间的关系和他们行动背后的原因的潜力。

电影故事理解的读写记忆网络

A Read-Write Memory Network for Movie Story Understanding

Machine comprehension(MC) style question answering is a representative
problem in natural language processing. Previous methods rarely spend time on
the improvement of encoding layer, especially the embedding of syntactic
information and name entity of the words, which are very crucial to the quality
of encoding. Moreover, existing attention methods represent each query word as
a vector or use a single vector to represent the whole query sentence, neither
of them can handle the proper weight of the key words in query sentence. In
this paper, we introduce a novel neural network architecture called Multi-layer
Embedding with Memory Network(MEMEN) for machine reading task. In the encoding
layer, we employ classic skip-gram model to the syntactic and semantic
information of the words to train a new kind of embedding layer. We also
propose a memory network of full-orientation matching of the query and passage
to catch more pivotal information. Experiments show that our model has
competitive results both from the perspectives of precision and efficiency in
Stanford Question Answering Dataset(SQuAD) among all published results and
achieves the state-of-the-art results on TriviaQA dataset.

本论文介绍了一种新型的神经网络架构 MEMEN，在 MC 风格的问题回答任务中取得了具有竞争力的结果，并在 TriviaQA 数据集上取得了最新成果。

MEMEN：用记忆网络的多层嵌入进行机器阅读理解

MEMEN: Multi-layer Embedding with Memory Networks for Machine  Comprehension

In this paper we address the question of how to render sequence-level
networks better at handling structured input. We propose a machine reading
simulator which processes text incrementally from left to right and performs
shallow reasoning with memory and attention. The reader extends the Long
Short-Term Memory architecture with a memory network in place of a single
memory cell. This enables adaptive memory usage during recurrence with neural
attention, offering a way to weakly induce relations among tokens. The system
is initially designed to process a single sequence but we also demonstrate how
to integrate it with an encoder-decoder architecture. Experiments on language
modeling, sentiment analysis, and natural language inference show that our
model matches or outperforms the state of the art.

论文提出了一种基于记忆网络和神经关注机制的机器阅读模拟器，能够更好地处理结构化输入，并结合编码器 - 解码器体系结构，取得了很好效果。

用于机器阅读的长短期记忆网络

Long Short-Term Memory-Networks for Machine Reading

We introduce a neural network with a recurrent attention model over a
possibly large external memory. The architecture is a form of Memory Network
(Weston et al., 2015) but unlike the model in that work, it is trained
end-to-end, and hence requires significantly less supervision during training,
making it more generally applicable in realistic settings. It can also be seen
as an extension of RNNsearch to the case where multiple computational steps
(hops) are performed per output symbol. The flexibility of the model allows us
to apply it to tasks as diverse as (synthetic) question answering and to
language modeling. For the former our approach is competitive with Memory
Networks, but with less supervision. For the latter, on the Penn TreeBank and
Text8 datasets our approach demonstrates comparable performance to RNNs and
LSTMs. In both cases we show that the key concept of multiple computational
hops yields improved results.

本文介绍了一种具有循环注意力模型的神经网络，该模型可以扩展到可能很大的外部存储器，并被应用于各种任务，如（合成）问答和语言建模。同时，本文展示了多个计算步骤（跳数）的关键概念可以提高性能表现。