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Found 20 papers, 5 papers with code
A Copy-Augmented Generative Model for Open-Domain Question Answering
Open-domain question answering is a challenging task with a wide variety of practical applications.
How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis
We check the words that have three typical associations with the missing words: knowledge-dependent, positionally close, and highly co-occurred.
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering
To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR).
MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting
As a result, the final method takes the advantage of effective semantic & image-level filling for high-fidelity inpainting.
Read before Generate! Faithful Long Form Question Answering with Machine Reading
Long-form question answering (LFQA) aims to generate a paragraph-length answer for a given question.
Unsupervised Open-Domain Question Answering
Open-domain Question Answering (ODQA) has achieved significant results in terms of supervised learning manner.
Machine Reading Comprehension
Open-Domain Question Answering
JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting
In this paper, for the first time, we formulate image inpainting as a mix of two problems, predictive filtering and deep generation.
Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation
It is still an open question to leverage various types of information under the BERT framework.
HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions
In this paper, we propose a new retrieval target, hop, to collect the hidden reasoning evidence from Wikipedia for complex question answering.
Ranked #6 on
Question Answering
on HotpotQA
Monitoring-based Differential Privacy Mechanism Against Query-Flooding Parameter Duplication Attack
In MDP, we first propose a novel real-time model extraction status assessment scheme called Monitor to evaluate the situation of the model.
SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval
Term-based sparse representations dominate the first-stage text retrieval in industrial applications, due to its advantage in efficiency, interpretability, and exact term matching.
Blur-Attention: A boosting mechanism for non-uniform blurred image restoration
Our method can adaptively select the weights of the extracted features according to the spatially varying blur features, and dynamically restore the images.
A lateral semicircular canal segmentation based geometric calibration for human temporal bone CT Image
Computed Tomography (CT) of the temporal bone has become an important method for diagnosing ear diseases.
Dynamics of ferromagnetic bimerons driven by spin currents and magnetic fields
Numerical simulations demonstrate that two bimerons with opposite signs of topological numbers can be created simultaneously in a ferromagnetic thin film via current-induced spin torques.
Mesoscale and Nanoscale Physics
Mitigating Query-Flooding Parameter Duplication Attack on Regression Models with High-Dimensional Gaussian Mechanism
Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through public queries.
DUMA: Reading Comprehension with Transposition Thinking
Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question.
Ranked #3 on
Reading Comprehension
on RACE
Dual Multi-head Co-attention for Multi-choice Reading Comprehension
Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question.
A Phase Shift Deep Neural Network for High Frequency Approximation and Wave Problems
In this paper, we propose a phase shift deep neural network (PhaseDNN), which provides a uniform wideband convergence in approximating high frequency functions and solutions of wave equations.
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
PhaseDNN - A Parallel Phase Shift Deep Neural Network for Adaptive Wideband Learning
Due to the phase shift, each DNN achieves the speed of convergence as in the low frequency range.
