1 code implementation • 13 Apr 2022 • Griffin Adams, Han-Chin Shing, Qing Sun, Christopher Winestock, Kathleen McKeown, Noémie Elhadad
We extract a small corpus from a noisy source--the Electronic Health Record (EHR)--for the task of summarizing a hospital admission from multiple notes.
no code implementations • 29 Sep 2021 • Qing Sun, Fan Lyu, Fanhua Shang, Wei Feng, Liang Wan
Traditionally, the primary goal of LL is to achieve the trade-off between the Stability (remembering past tasks) and Plasticity (adapting to new tasks).
no code implementations • 29 Sep 2021 • Qing Sun
Deep neural networks have achieved impressive performance on a variety of domains.
no code implementations • Findings (ACL) 2021 • Qing Sun, Parminder Bhatia
Our gazetteer based fusion model is data efficient, achieving +1. 7 micro-F1 gains on the i2b2 dataset using 20% training data, and brings + 4. 7 micro-F1 gains on novel entity mentions never presented during training.
1 code implementation • EMNLP 2020 • Kristjan Arumae, Qing Sun, Parminder Bhatia
However, in order to achieve state-of-the-art performance on out of domain tasks such as clinical named entity recognition and relation extraction, additional in domain pre-training is required.
no code implementations • 23 Aug 2020 • Qing Sun, James Cross
In this paper, we provide an in-depth analysis of KL-divergence minimization in Forward and Backward orders, which shows that learners are reinforced via on-policy learning in Backward.
no code implementations • 25 Sep 2019 • Qing Sun, James Cross, Dmitriy Genzel
Sequence-to-sequence models such as transformers, which are now being used in a wide variety of NLP tasks, typically need to have very high capacity in order to perform well.
no code implementations • CVPR 2017 • Qing Sun, Stefan Lee, Dhruv Batra
We develop the first approximate inference algorithm for 1-Best (and M-Best) decoding in bidirectional neural sequence models by extending Beam Search (BS) to reason about both forward and backward time dependencies.
20 code implementations • 7 Oct 2016 • Ashwin K. Vijayakumar, Michael Cogswell, Ramprasath R. Selvaraju, Qing Sun, Stefan Lee, David Crandall, Dhruv Batra
We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
no code implementations • NeurIPS 2015 • Qing Sun, Dhruv Batra
This paper formulates the search for a set of bounding boxes (as needed in object proposal generation) as a monotone submodular maximization problem over the space of all possible bounding boxes in an image.