422 research outputs found
Efficient Data Learning for Open Information Extraction with Pre-trained Language Models
Open Information Extraction (OpenIE) is a fundamental yet challenging task in
Natural Language Processing, which involves extracting all triples (subject,
predicate, object) from a given sentence. While labeling-based methods have
their merits, generation-based techniques offer unique advantages, such as the
ability to generate tokens not present in the original sentence. However, these
generation-based methods often require a significant amount of training data to
learn the task form of OpenIE and substantial training time to overcome slow
model convergence due to the order penalty. In this paper, we introduce a novel
framework, OK-IE, that ingeniously transforms the task form of OpenIE into the
pre-training task form of the T5 model, thereby reducing the need for extensive
training data. Furthermore, we introduce an innovative concept of Anchor to
control the sequence of model outputs, effectively eliminating the impact of
order penalty on model convergence and significantly reducing training time.
Experimental results indicate that, compared to previous SOTA methods, OK-IE
requires only 1/100 of the training data (900 instances) and 1/120 of the
training time (3 minutes) to achieve comparable results
Efficient Algorithms for Sparse Moment Problems without Separation
We consider the sparse moment problem of learning a -spike mixture in
high-dimensional space from its noisy moment information in any dimension. We
measure the accuracy of the learned mixtures using transportation distance.
Previous algorithms either assume certain separation assumptions, use more
recovery moments, or run in (super) exponential time. Our algorithm for the
one-dimensional problem (also called the sparse Hausdorff moment problem) is a
robust version of the classic Prony's method, and our contribution mainly lies
in the analysis. We adopt a global and much tighter analysis than previous work
(which analyzes the perturbation of the intermediate results of Prony's
method). A useful technical ingredient is a connection between the linear
system defined by the Vandermonde matrix and the Schur polynomial, which allows
us to provide tight perturbation bound independent of the separation and may be
useful in other contexts. To tackle the high-dimensional problem, we first
solve the two-dimensional problem by extending the one-dimensional algorithm
and analysis to complex numbers. Our algorithm for the high-dimensional case
determines the coordinates of each spike by aligning a 1d projection of the
mixture to a random vector and a set of 2d projections of the mixture. Our
results have applications to learning topic models and Gaussian mixtures,
implying improved sample complexity results or running time over prior work
UCF: Uncovering Common Features for Generalizable Deepfake Detection
Deepfake detection remains a challenging task due to the difficulty of
generalizing to new types of forgeries. This problem primarily stems from the
overfitting of existing detection methods to forgery-irrelevant features and
method-specific patterns. The latter has been rarely studied and not well
addressed by previous works. This paper presents a novel approach to address
the two types of overfitting issues by uncovering common forgery features.
Specifically, we first propose a disentanglement framework that decomposes
image information into three distinct components: forgery-irrelevant,
method-specific forgery, and common forgery features. To ensure the decoupling
of method-specific and common forgery features, a multi-task learning strategy
is employed, including a multi-class classification that predicts the category
of the forgery method and a binary classification that distinguishes the real
from the fake. Additionally, a conditional decoder is designed to utilize
forgery features as a condition along with forgery-irrelevant features to
generate reconstructed images. Furthermore, a contrastive regularization
technique is proposed to encourage the disentanglement of the common and
specific forgery features. Ultimately, we only utilize the common forgery
features for the purpose of generalizable deepfake detection. Extensive
evaluations demonstrate that our framework can perform superior generalization
than current state-of-the-art methods
Dielectric response of soft mode in ferroelectric SrTiO3
We report far-infrared dielectric properties of powder form ferroelectric
SrTiO3. Terahertz time-domain spectroscopy (THz-TDS) measurement reveals that
the low-frequency dielectric response of SrTiO3 is a consequence of the lowest
transverse optical (TO) soft mode TO1 at 2.70 THz (90.0 1/cm), which is
directly verified by Raman spectroscopy. This result provides a better
understanding of the relation of low-frequency dielectric function with the
optical phonon soft mode for ferroelectric materials. Combining THz-TDS with
Raman spectra, the overall low-frequency optical phonon response of SrTiO3 is
presented in an extended spectral range from 6.7 1/cm to 1000.0 1/cm.Comment: 14 pages; 4 figure
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