1.8k citations · 3.8k across the 47 of their papers we have counts for
8 papers · 1 filter
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent
Zhilin Yang, Saizheng Zhang, Jack Urbanek +5
Contrary to most natural language processing research, which makes use of static datasets, humans learn language interactively, grounded in an environment. In this work we propose…
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov +1
We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is lim…
Good Semi-supervised Learning that Requires a Bad GAN
Zihang Dai, Zhilin Yang, Fan Yang +2
Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint…
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
Zhilin Yang, Ruslan Salakhutdinov, William W. Cohen
Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks. One appealing property of such systems is their gener…
Linguistic Knowledge as Memory for Recurrent Neural Networks
Bhuwan Dhingra, Zhilin Yang, William W. Cohen +1
Training recurrent neural networks to model long term dependencies is difficult. Hence, we propose to use external linguistic knowledge as an explicit signal to inform the model wh…
Differentiable Learning of Logical Rules for Knowledge Base Reasoning
Fan Yang, Zhilin Yang, William W. Cohen
We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning. This learning problem is difficult because it requires learning the parameter…
A Probabilistic Framework for Location Inference from Social Media
Yujie Qian, Jie Tang, Zhilin Yang +3
We study the extent to which we can infer users' geographical locations from social media. Location inference from social media can benefit many applications, such as disaster mana…
Semi-Supervised QA with Generative Domain-Adaptive Nets
Zhilin Yang, Junjie Hu, Ruslan Salakhutdinov +1
We study the problem of semi-supervised question answering----utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework,…