no code implementations • CONLL 2019 • Daniel Gillick, Sayali Kulkarni, Larry Lansing, Alessandro Presta, Jason Baldridge, Eugene Ie, Diego Garcia-Olano
We show that it is feasible to perform entity linking by training a dual encoder (two-tower) model that encodes mentions and entities in the same dense vector space, where candidate entities are retrieved by approximate nearest neighbor search.
1 code implementation • 6 Dec 2019 • Larry Lansing, Vihan Jain, Harsh Mehta, Haoshuo Huang, Eugene Ie
VALAN is a lightweight and scalable software framework for deep reinforcement learning based on the SEED RL architecture.
1 code implementation • ACL 2020 • Dan Iter, Kelvin Guu, Larry Lansing, Dan Jurafsky
Recent models for unsupervised representation learning of text have employed a number of techniques to improve contextual word representations but have put little focus on discourse-level representations.