371 research outputs found
Enhancing Phrase Representation by Information Bottleneck Guided Text Diffusion Process for Keyphrase Extraction
Keyphrase extraction (KPE) is an important task in Natural Language
Processing for many scenarios, which aims to extract keyphrases that are
present in a given document. Many existing supervised methods treat KPE as
sequential labeling, span-level classification, or generative tasks. However,
these methods lack the ability to utilize keyphrase information, which may
result in biased results. In this study, we propose Diff-KPE, which leverages
the supervised Variational Information Bottleneck (VIB) to guide the text
diffusion process for generating enhanced keyphrase representations. Diff-KPE
first generates the desired keyphrase embeddings conditioned on the entire
document and then injects the generated keyphrase embeddings into each phrase
representation. A ranking network and VIB are then optimized together with rank
loss and classification loss, respectively. This design of Diff-KPE allows us
to rank each candidate phrase by utilizing both the information of keyphrases
and the document. Experiments show that Diff-KPE outperforms existing KPE
methods on a large open domain keyphrase extraction benchmark, OpenKP, and a
scientific domain dataset, KP20K.Comment: 10 pages, 2 figure
Sequential Copying Networks
Copying mechanism shows effectiveness in sequence-to-sequence based neural
network models for text generation tasks, such as abstractive sentence
summarization and question generation. However, existing works on modeling
copying or pointing mechanism only considers single word copying from the
source sentences. In this paper, we propose a novel copying framework, named
Sequential Copying Networks (SeqCopyNet), which not only learns to copy single
words, but also copies sequences from the input sentence. It leverages the
pointer networks to explicitly select a sub-span from the source side to target
side, and integrates this sequential copying mechanism to the generation
process in the encoder-decoder paradigm. Experiments on abstractive sentence
summarization and question generation tasks show that the proposed SeqCopyNet
can copy meaningful spans and outperforms the baseline models.Comment: In AAAI 201
Ferromagnetic, structurally disordered ZnO implanted with Co ions
We present superparamagnetic clusters of structurally highly disordered
Co-Zn-O created by high fluence Co ion implantation into ZnO (0001) single
crystals at low temperatures. This secondary phase cannot be detected by common
x-ray diffraction but is observed by high-resolution transmission electron
microscopy. In contrast to many other secondary phases in a ZnO matrix it
induces low-field anomalous Hall effect and thus is a candidate for
magneto-electronics applications.Comment: 5 pages, 3 figure
A duplication-free quantum neural network for universal approximation
The universality of a quantum neural network refers to its ability to
approximate arbitrary functions and is a theoretical guarantee for its
effectiveness. A non-universal neural network could fail in completing the
machine learning task. One proposal for universality is to encode the quantum
data into identical copies of a tensor product, but this will substantially
increase the system size and the circuit complexity. To address this problem,
we propose a simple design of a duplication-free quantum neural network whose
universality can be rigorously proved. Compared with other established
proposals, our model requires significantly fewer qubits and a shallower
circuit, substantially lowering the resource overhead for implementation. It is
also more robust against noise and easier to implement on a near-term device.
Simulations show that our model can solve a broad range of classical and
quantum learning problems, demonstrating its broad application potential.Comment: 15 pages, 10 figure
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