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CEDR: Contextualized Embeddings for Document Ranking
Sean MacAvaney, Andrew Yates, Arman Cohan, Nazli GoharianSIGIR • 2019 Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized…Ontology-Aware Clinical Abstractive Summarization
Sean MacAvaney, Sajad Sotudeh, Arman Cohan, Nazli Goharian, Ish Talati, Ross W. FiliceSIGIR • 2019 Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors. We propose a sequence-to-sequence abstractive summarization model augmented with domain-specific ontological…Exploiting Explicit Paths for Multi-hop Reading Comprehension
Souvik Kundu, Tushar Khot, Ashish Sabharwal, Peter ClarkACL • 2019 We propose a novel, path-based reasoning approach for the multi-hop reading comprehension task where a system needs to combine facts from multiple passages to answer a question. Although inspired by multi-hop reasoning over knowledge graphs, our proposed…Quantifying Sex Bias in Clinical Studies at Scale With Automated Data Extraction
Sergey Feldman, Waleed Ammar, Kyle Lo, Elly Trepman, Madeleine van Zuylen, Oren EtzioniJAMA • 2019 Importance: Analyses of female representation in clinical studies have been limited in scope and scale. Objective: To perform a large-scale analysis of global enrollment sex bias in clinical studies. Design, Setting, and Participants: In this cross…Cooperative Generator-Discriminator Networks for Abstractive Summarization with Narrative Flow
Saadia Gabriel, Antoine Bosselut, Ari Holtzman, Kyle Lo, Asli Çelikyilmaz, Yejin ChoiarXiv • 2019 We introduce Cooperative Generator-Discriminator Networks (Co-opNet), a general framework for abstractive summarization with distinct modeling of the narrative flow in the output summary. Most current approaches to abstractive summarization, in contrast, are…Efficient Adaptation of Pretrained Transformers for Abstractive Summarization
Andrew Pau Hoang, Antoine Bosselut, Asli Çelikyilmaz, Yejin ChoiarXiv • 2019 Large-scale learning of transformer language models has yielded improvements on a variety of natural language understanding tasks. Whether they can be effectively adapted for summarization, however, has been less explored, as the learned representations are…Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors
Mohammad Mahdi Derakhshani, Saeed Masoudnia, Amir Hossein Shaker, Omid Mersa, Mohammad Amin Sadeghi, Mohammad Rastegari, Babak N. AraabiCVPR • 2019 We present a simple and effective learning technique that significantly improves mAP of YOLO object detectors without compromising their speed. During network training, we carefully feed in localization information. We excite certain activations in order to…ELASTIC: Improving CNNs by Instance Specific Scaling Policies
Huiyu Wang, Aniruddha Kembhavi, Ali Farhadi, Alan Loddon Yuille, Mohammad RastegariCVPR • 2019 Scale variation has been a challenge from traditional to modern approaches in computer vision. Most solutions to scale issues have similar theme: a set of intuitive and manually designed policies that are generic and fixed (e.g. SIFT or feature pyramid). We…ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
Sachin Mehta, Mohammad Rastegari, Linda Shapiro, Hannaneh HajishirziCVPR • 2019 We introduce a light-weight, power efficient, and general purpose convolutional neural network, ESPNetv2 , for modeling visual and sequential data. Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a…From Recognition to Cognition: Visual Commonsense Reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, Yejin ChoiCVPR • 2019 Visual understanding goes well beyond object recognition. With one glance at an image, we can effortlessly imagine the world beyond the pixels: for instance, we can infer people’s actions, goals, and mental states. While this task is easy for humans, it is…