62 citations · 62 across the 3 of their papers we have counts for
7 papers
Meta-Learning Fast Weight Language Models
Kevin Clark, Kelvin Guu, Ming-Wei Chang +3
Dynamic evaluation of language models (LMs) adapts model parameters at test time using gradient information from previous tokens and substantially improves LM performance. However,…
Pre-Training Transformers as Energy-Based Cloze Models
Kevin Clark, Minh-Thang Luong, Quoc V. Le +1
We introduce Electric, an energy-based cloze model for representation learning over text. Like BERT, it is a conditional generative model of tokens given their contexts. However, E…
BAM! Born-Again Multi-Task Networks for Natural Language Understanding
Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal +2
It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts. To help address this, we propose using knowledge distillatio…
What Does BERT Look At? An Analysis of BERT's Attention
Kevin Clark, Urvashi Khandelwal, Omer Levy +1
Large pre-trained neural networks such as BERT have had great recent success in NLP, motivating a growing body of research investigating what aspects of language they are able to l…
Sample Efficient Text Summarization Using a Single Pre-Trained Transformer
Urvashi Khandelwal, Kevin Clark, Dan Jurafsky +1
Language model (LM) pre-training has resulted in impressive performance and sample efficiency on a variety of language understanding tasks. However, it remains unclear how to best…
Semi-Supervised Sequence Modeling with Cross-View Training
Kevin Clark, Minh-Thang Luong, Christopher D. Manning +1
Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models, mainly because they can take advantage of large amount…