End-to-End Natural Language Generation for Conversational Agents

Last updated on Nov 27, 2020

Dataset : E2E NLG Challenge

Each instance consist of a dialogue act-based meaning representation (MR) and up to 5 references in natural language

MR:

name[The Eagle],
eatType[coffee shop],
food[French],
priceRange[moderate],
customerRating[3/5],
area[riverside],
kidsFriendly[yes],
near[Burger King]

NL:

The three star coffee shop, The Eagle, gives families a mid-priced dining experience 
featuring a variety of wines and cheeses.  Find The Eagle near Burger King.”
  • Train an Encoder-Decoder model for generating natural language response from MR
  • Encoder and decoder is based on GRU and utilized teacher forcing while training
  • Evaluate task on BLEU score
GRU Seq-to-Seq Deep Learning NLP

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