University of Cambridge > Talks.cam > NLIP Seminar Series > Learning from Past: Bringing Planning Back to Neural Generators

Learning from Past: Bringing Planning Back to Neural Generators

Add to your list(s) Download to your calendar using vCal

  • UserShashi Narayan (Google Research) World_link
  • ClockFriday 25 February 2022, 12:00-13:00
  • HouseVirtual (Zoom).

If you have a question about this talk, please contact Michael Schlichtkrull.

Traditional NLG systems in Reiter and Dale’s vision were inherently grounded and controllable, thanks to a planning stage which played a crucial role in ordering and structuring the information, and in grounding the generation of text to the plan. Modern neural generation systems have advanced NLG beyond our imagination, yet some of the most desired properties such as grounding and controllability have been lost and are still to be mastered. In this talk, I will discuss why we need to bring back planning to neural generation and to make generation systems more grounded, controllable, inspectable and trustworthy. I will present several pieces of evidence supporting this direction exploring existing work in data-to-text and story generation, and in summarization.

Join Zoom Meeting https://cl-cam-ac-uk.zoom.us/j/95478516450?pwd=RGVuOFdmVFBLRzJXbUZ2a0dZR2tEQT09

Meeting ID: 954 7851 6450 Passcode: 123389

One tap mobile +12532158782,,95478516450#,,,,123389# US (Tacoma) +13017158592,,95478516450#,,,,123389# US (Washington DC)

Dial by your location +1 253 215 8782 US (Tacoma) +1 301 715 8592 US (Washington DC) +1 312 626 6799 US (Chicago) +1 346 248 7799 US (Houston) +1 669 900 6833 US (San Jose) +1 929 205 6099 US (New York) Meeting ID: 954 7851 6450 Passcode: 123389 Find your local number: https://cl-cam-ac-uk.zoom.us/u/agr3zvBDM

This talk is part of the NLIP Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2024 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity