From 9729e15c537f7b646ddbca524669cb820d7cffa8 Mon Sep 17 00:00:00 2001 From: vishwacsena Date: Sat, 4 May 2019 18:29:39 -0700 Subject: [PATCH] Update README.md --- experimental/language-generation/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experimental/language-generation/README.md b/experimental/language-generation/README.md index ca883d2824..0b7c82db43 100644 --- a/experimental/language-generation/README.md +++ b/experimental/language-generation/README.md @@ -1,5 +1,5 @@ # Language Generation ***_[PREVIEW]_*** -Language generation is the umbrella term for generating appropriate output to the user. In some sense, language generation is reverse of language understanding. While language understanding goes from user input to extracting meaningful information such as intent and entities, language generation helps construct meaningful, variable and grammatically correct responses that a bot can send back to the user. +Learning from our customers experiences and bringing together capabilities first implemented by Cortana and Cognition teams, we are introducing Language Generation; which allows the developer to extract the embedded strings from their code and resource files and manage them through a Language Generation runtime and file format. Language Generation enable customers to define multiple variations on a phrase, execute simple expressions based on context, refer to conversational memory, and over time will enable us to bring additional capabilities all leading to a more natural conversational experience. At the core of language generation lies template expansion and entity substitution. You can provide one-of variation for expansion as well as conditionally expand a template. The output from language generation can be a simple text string or multi-line response or a complex object payload that a layer above language generation will use to construct a full blown [activity][1].