NLG week3

NLG enables machines to convert structured data into human text or speech.
It's objective is machine creation of natural language so that they can do something useful. Challenge is the understanding and representing the meaning of the word. NLG system input can be defined as a four-tuple such as knowledge source, communicative goal, user model, discourse history.
Let see the NLG pipeline as the simple view. First, the purpose of communication motivation. Second, What do we want to say? Content or message. Third, How are we going to say it? Finally, Produce communication formulating the output.
The goal, knowledge of domain and grammar used to generate the document planning.
Why is NLG difficult? Mapping, choice, planning problems.
NLG application contains Machine translation, authoring, text summarization, Question answering in the knowledge base.
To deploy the nlg, you have to think what the objective is? And what is the data to process? What is the output? Will the benefit offer a return on investment?
The computer must behave intelligently that act like humans to achieve human-level performance in all cognitive tasks. It includes NLP, knowledge representation, automated reasoning, machine learning.

Alan Turing and the Imitation Game
Turing Test - Still valid?
Measures of intelligence
The problem with the Turing Test

NLG article:  a detailed survey of the field

Marco planning - what do you want to say?
 : Content determination Discourse planning
Micro planning  - how will you say it?
 : Lexicalization, Aggregation, referring expressions 
Realization - what will you actually say?

NLG Application
- Summarization
- Content Creation
- Conversational Interfaces(Chabots)

NLG has two approaches. For example GOFAI techniques such as rules and templates, and the second using a type of Neural Network called a Recurrent Neural Network.

Yufeng interviews Google Research engineer Justin Zhao (Links to an external site.)Links to an external site. to talk NLG and their use of recurrent neural networks
 Templates vs. NLG (where NLG means machine learning) by Ehud Reiter

 this article by Andrej Karpathy (Links to an external site.)Links to an external site.: simple RNN
 excellent article on Github by Christopher Olah (Links to an external site.)Links to an external site. of Google Brain of the issues of RNNs


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