Steering Output Style and Topic in Neural Response Generation
Di Wang, Nebojša Jojić, Chris Brockett, Eric Nyberg · 2017
We propose simple and flexible training and decoding methods for influencing output style and topic in neural encoderdecoder based language generation.This capability is desirable in a variety of applications, including conversational systems, where successful agents need to produce language in a specific style and generate responses steered by a human puppeteer or external knowledge.We decompose the neural generation process into empirically easier sub-problems: a faithfulness model and a decoding method based on selectivesampling.We also describe training and sampling algorithms that bias the generation process with a specific language style restriction, or a topic restriction.Human evaluation results show that our proposed methods are able to to restrict style and topic without degrading output quality in conversational tasks.chat context Where are you?Twitter bot i'm on my way to london now.Star Wars bot we're heading to the dark side of jabba's palace.Hillary bot i'm here in philadelphia, pennsylvania Trump bot i went to the white house.we were in brooklyn.Kennedy bot i am on the question of the united states.chat context What are you doing?Twitter bot i'm just chilling with my dad, i'm just sitting in the kitchen and listening to music Star Wars bot i'm doing my magic solo.Hillary bot i'm running for president, i'm going to be talking about some of these things Trump bot i'm building a wall, right now Kennedy bot today, i am asking the congress for a new program to make a new effort to increase the tax privileges and to stimulate