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NLG ¶ NLG stands for Natural Languate Generation. NLG is one field of AI aims to generate the understandable and appropriate texts from raw data. We should differientiate the concepts of NLG with NLP and NLU. NLP is natural languate processing. This is a field in AI working on text generally. NLP contains Speed recornigion, Speed synthesis, NLG and NLU. NLU and NLG is subsets of NLP. While NLG generate the text, NLU uses text as input and generate some pattern such as Sentiment Analysis, Summary. The pipeline of NLG NLG can be divided into 3 phases: Document planning, Microplanning and Realisation. The purpose of Document planning is to chose what to say and the purpos of Microplanning and Realisation is to find how to say. There are some components in each phase. In traditional NLG system, we have 5 components: Content Determination, Text Structure, Aggregation, Lexicalisation, Reffering expression, Realisation. Content Determination Content Determination is sets of enti...

Generative Adversarial Networks (GAN) is one a Neural Network architecture which simulates zero-sum game. There are 2 parts of this Neural Network. The first is called Generator and other is called Discriminator. Generator tries to mimic data and make the fake data likes the real data in distribution. Meanwhile, the Discriminator tries to maximize the difference between real data and fake data. It is reason we call zero-sum game. Two parts are coaction with each other. This structure makes the GAN to be a interesting Neural Network architecture and it has many application in both academic and industry. In modeling, the GAN is an approach of equilibrium Networks such as Boltzmann Machine did. It is an optimization problem with objectives of: minimize Generator and maximum Discriminator simultaneously.  $max_{D}min_{G} V(D,G)$  $max_{D}min_{G} V(D,G) = E_{x\sim p_{data}(x)}[log(D(x))] + E_{z\sim p_{z}(z)}[log(1 - D(G(z)))]$ $V(D,G)$ is optimization problem subject to G ...