no code implementations • 5 Mar 2022 • Qifan Wang, Yi Fang, Anirudh Ravula, Ruining He, Bin Shen, Jingang Wang, Xiaojun Quan, Dongfang Liu
Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks.
no code implementations • 1 Feb 2022 • Qifan Wang, Yi Fang, Anirudh Ravula, Fuli Feng, Xiaojun Quan, Dongfang Liu
Structure information extraction refers to the task of extracting structured text fields from web pages, such as extracting a product offer from a shopping page including product title, description, brand and price.
no code implementations • EACL 2021 • Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, Ilya Eckstein
This enables a new class of powerful, high-capacity representations that can ultimately distill much of the useful information about an entity from multiple text sources, without any human supervision.
5 code implementations • Findings (ACL) 2021 • Ruining He, Anirudh Ravula, Bhargav Kanagal, Joshua Ainslie
Transformer is the backbone of modern NLP models.
Ranked #4 on Paraphrase Identification on Quora Question Pairs
11 code implementations • NeurIPS 2020 • Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed
To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
Ranked #1 on Text Classification on Arxiv HEP-TH citation graph
2 code implementations • EMNLP 2020 • Joshua Ainslie, Santiago Ontanon, Chris Alberti, Vaclav Cvicek, Zachary Fisher, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, Li Yang
Transformer models have advanced the state of the art in many Natural Language Processing (NLP) tasks.
Ranked #3 on Question Answering on ConditionalQA