519 citations · 2.9k across the 162 of their papers we have counts for
36 papers · 2 filters
Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations
Siddhant Arora, Danish Pruthi, Norman Sadeh +3
In attempts to "explain" predictions of machine learning models, researchers have proposed hundreds of techniques for attributing predictions to features that are deemed important.…
DEEP: DEnoising Entity Pre-training for Neural Machine Translation
Junjie Hu, Hiroaki Hayashi, Kyunghyun Cho +1
It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. Earlier named entity translation…
Lexically Aware Semi-Supervised Learning for OCR Post-Correction
Shruti Rijhwani, Daisy Rosenblum, Antonios Anastasopoulos +1
Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents. Optical character recognition (OCR) can be used to produce…
On The Ingredients of an Effective Zero-shot Semantic Parser
Pengcheng Yin, John Wieting, Avirup Sil +1
Semantic parsers map natural language utterances into meaning representations (e.g., programs). Such models are typically bottlenecked by the paucity of training data due to the re…
Systematic Inequalities in Language Technology Performance across the World's Languages
Damián Blasi, Antonios Anastasopoulos, Graham Neubig
Natural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and de…
Breaking Down Multilingual Machine Translation
Ting-Rui Chiang, Yi-Pei Chen, Yi-Ting Yeh +1
While multilingual training is now an essential ingredient in machine translation (MT) systems, recent work has demonstrated that it has different effects in different multilingual…