Covidex: Neural Ranking Models and Keyword Search Infrastructure for the
COVID-19 Open Research Dataset
- URL: http://arxiv.org/abs/2007.07846v1
- Date: Tue, 14 Jul 2020 16:26:01 GMT
- Title: Covidex: Neural Ranking Models and Keyword Search Infrastructure for the
COVID-19 Open Research Dataset
- Authors: Edwin Zhang, Nikhil Gupta, Raphael Tang, Xiao Han, Ronak Pradeep,
Kuang Lu, Yue Zhang, Rodrigo Nogueira, Kyunghyun Cho, Hui Fang, Jimmy Lin
- Abstract summary: Covidex is a search engine that exploits the latest neural ranking models.
It provides access to the COVID-19 Open Research dataset curated by the Allen Institute for AI.
- Score: 87.47567807116204
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present Covidex, a search engine that exploits the latest neural ranking
models to provide information access to the COVID-19 Open Research Dataset
curated by the Allen Institute for AI. Our system has been online and serving
users since late March 2020. The Covidex is the user application component of
our three-pronged strategy to develop technologies for helping domain experts
tackle the ongoing global pandemic. In addition, we provide robust and
easy-to-use keyword search infrastructure that exploits mature fusion-based
methods as well as standalone neural ranking models that can be incorporated
into other applications. These techniques have been evaluated in the ongoing
TREC-COVID challenge: Our infrastructure and baselines have been adopted by
many participants, including some of the highest-scoring runs in rounds 1, 2,
and 3. In round 3, we report the highest-scoring run that takes advantage of
previous training data and the second-highest fully automatic run.
Related papers
- Combining Neural Architecture Search and Automatic Code Optimization: A Survey [0.8796261172196743]
Two notable techniques are Hardware-aware Neural Architecture Search (HW-NAS) and Automatic Code Optimization (ACO)
HW-NAS automatically designs accurate yet hardware-friendly neural networks, while ACO involves searching for the best compiler optimizations to apply on neural networks.
This survey explores recent works that combine these two techniques within a single framework.
arXiv Detail & Related papers (2024-08-07T22:40:05Z) - Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective [111.58315434849047]
robustness of neural information retrieval models (IR) models has garnered significant attention.
We view the robustness of IR to be a multifaceted concept, emphasizing its necessity against adversarial attacks, out-of-distribution (OOD) scenarios and performance variance.
We provide an in-depth discussion of existing methods, datasets, and evaluation metrics, shedding light on challenges and future directions in the era of large language models.
arXiv Detail & Related papers (2024-07-09T16:07:01Z) - OTOv3: Automatic Architecture-Agnostic Neural Network Training and
Compression from Structured Pruning to Erasing Operators [57.145175475579315]
This topic spans various techniques, from structured pruning to neural architecture search, encompassing both pruning and erasing operators perspectives.
We introduce the third-generation Only-Train-Once (OTOv3), which first automatically trains and compresses a general DNN through pruning and erasing operations.
Our empirical results demonstrate the efficacy of OTOv3 across various benchmarks in structured pruning and neural architecture search.
arXiv Detail & Related papers (2023-12-15T00:22:55Z) - IRT2: Inductive Linking and Ranking in Knowledge Graphs of Varying Scale [1.3621712165154805]
We address the challenge of building domain-specific knowledge models for industrial use cases.
Our focus is on inductive link prediction models as a basis for practical tools.
arXiv Detail & Related papers (2023-01-02T15:19:21Z) - CorpusBrain: Pre-train a Generative Retrieval Model for
Knowledge-Intensive Language Tasks [62.22920673080208]
Single-step generative model can dramatically simplify the search process and be optimized in end-to-end manner.
We name the pre-trained generative retrieval model as CorpusBrain as all information about the corpus is encoded in its parameters without the need of constructing additional index.
arXiv Detail & Related papers (2022-08-16T10:22:49Z) - Safer Illinois and RokWall: Privacy Preserving University Health Apps
for COVID-19 [24.12822717216725]
COVID-19 has fundamentally disrupted the way we live. Government bodies, universities, and companies are rapidly developing technologies to combat the COVID-19 pandemic and safely reopen society.
Essential analytics tools such as contact tracing, super-spreader event detection, and exposure mapping require collecting and analyzing sensitive user information.
We analyze two such computing infrastructures under development at the University of Illinois at Urbana-Champaign to track and mitigate the spread of COVID-19.
arXiv Detail & Related papers (2021-01-19T23:36:14Z) - OpenHoldem: An Open Toolkit for Large-Scale Imperfect-Information Game
Research [82.09426894653237]
OpenHoldem is an integrated toolkit for large-scale imperfect-information game research using NLTH.
OpenHoldem makes three main contributions to this research direction: 1) a standardized evaluation protocol for thoroughly evaluating different NLTH AIs, 2) three publicly available strong baselines for NLTH AI, and 3) an online testing platform with easy-to-use APIs for public NLTH AI evaluation.
arXiv Detail & Related papers (2020-12-11T07:24:08Z) - An Adaptive Intelligence Algorithm for Undersampled Knee MRI
Reconstruction [4.5887393876309375]
In this work, we present the application of adaptive intelligence to accelerate MR acquisition.
We adopt deep neural networks to refine and correct prior reconstruction assumptions given the training data.
The network was trained and tested on a knee MRI dataset from the 2019 fastMRI challenge organized by Facebook AI Research and NYU Langone Health.
arXiv Detail & Related papers (2020-04-15T20:59:56Z) - Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research
Dataset: Preliminary Thoughts and Lessons Learned [88.42878484408469]
We present the Neural Covidex, a search engine that exploits the latest neural ranking architectures.
This paper describes our initial efforts and offers a few thoughts about lessons we have learned along the way.
arXiv Detail & Related papers (2020-04-10T17:12:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.