704 citations · 706 across the 3 of their papers we have counts for
4 papers
Better Word Embeddings by Disentangling Contextual n-Gram Information
Prakhar Gupta, Matteo Pagliardini, Martin Jaggi
Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, we show how training word embeddings jointly with bigram and even trigram embedd…
Online Diverse Learning to Rank from Partial-Click Feedback
Prakhar Gupta, Gaurush Hiranandani, Harvineet Singh +3
Learning to rank is an important problem in machine learning and recommender systems. In a recommender system, a user is typically recommended a list of items. Since the user is un…
Learning Word Vectors for 157 Languages
Edouard Grave, Piotr Bojanowski, Prakhar Gupta +2
Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredien…
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
Matteo Pagliardini, Prakhar Gupta, Martin Jaggi
The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (…