241 citations · 983 across the 67 of their papers we have counts for
4 papers · 1 filter
Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training
Margaret Li, Stephen Roller, Ilia Kulikov +4
Generative dialogue models currently suffer from a number of problems which standard maximum likelihood training does not address. They tend to produce generations that (i) rely to…
Neural Text Generation with Unlikelihood Training
Sean Welleck, Ilia Kulikov, Stephen Roller +3
Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core. In particular, standard likelihood training and de…
Sequential Graph Dependency Parser
Sean Welleck, Kyunghyun Cho
We propose a method for non-projective dependency parsing by incrementally predicting a set of edges. Since the edges do not have a pre-specified order, we propose a set-based lear…
Non-Monotonic Sequential Text Generation
Sean Welleck, Kianté Brantley, Hal Daumé +1
Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a…