62 citations · 261 across the 9 of their papers we have counts for
9 papers · 1 filter
Few-Shot Recalibration of Language Models
Xiang Lisa Li, Urvashi Khandelwal, Kelvin Guu
Recent work has uncovered promising ways to extract well-calibrated confidence estimates from language models (LMs), where the model's confidence score reflects how likely it is to…
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
Kenton Lee, Mandar Joshi, Iulia Turc +7
Visually-situated language is ubiquitous -- sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to th…
With Little Power Comes Great Responsibility
Dallas Card, Peter Henderson, Urvashi Khandelwal +3
Despite its importance to experimental design, statistical power (the probability that, given a real effect, an experiment will reject the null hypothesis) has largely been ignored…
Nearest Neighbor Machine Translation
Urvashi Khandelwal, Angela Fan, Dan Jurafsky +2
We introduce -nearest-neighbor machine translation (NN-MT), which predicts tokens with a nearest neighbor classifier over a large datastore of cached examples, using represen…
Generalization through Memorization: Nearest Neighbor Language Models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky +2
We introduce NN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a -nearest neighbors (NN) model. The nearest neighbors are com…
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…
Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context
Urvashi Khandelwal, He He, Peng Qi +1
We know very little about how neural language models (LM) use prior linguistic context. In this paper, we investigate the role of context in an LSTM LM, through ablation studies. S…