19 citations · 46 across the 6 of their papers we have counts for
6 papers
Fix your Models by Fixing your Datasets
Atindriyo Sanyal, Vikram Chatterji, Nidhi Vyas +3
The quality of underlying training data is very crucial for building performant machine learning models with wider generalizabilty. However, current machine learning (ML) tools lac…
Namesakes: Ambiguously Named Entities from Wikipedia and News
Oleg Vasilyev, Aysu Altun, Nidhi Vyas +3
We present Namesakes, a dataset of ambiguously named entities obtained from English-language Wikipedia and news articles. It consists of 58862 mentions of 4148 unique entities and…
Training With Data Dependent Dynamic Learning Rates
Shreyas Saxena, Nidhi Vyas, Dennis DeCoste
Recently many first and second order variants of SGD have been proposed to facilitate training of Deep Neural Networks (DNNs). A common limitation of these works stem from the fact…
Learning Soft Labels via Meta Learning
Nidhi Vyas, Shreyas Saxena, Thomas Voice
One-hot labels do not represent soft decision boundaries among concepts, and hence, models trained on them are prone to overfitting. Using soft labels as targets provide regulariza…
Measuring Bias in Contextualized Word Representations
Keita Kurita, Nidhi Vyas, Ayush Pareek +2
Contextual word embeddings such as BERT have achieved state of the art performance in numerous NLP tasks. Since they are optimized to capture the statistical properties of training…
The ARIEL-CMU Systems for LoReHLT18
Aditi Chaudhary, Siddharth Dalmia, Junjie Hu +27
This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery a…