2,970 research outputs found
Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding
Abstractive community detection is an important spoken language understanding
task, whose goal is to group utterances in a conversation according to whether
they can be jointly summarized by a common abstractive sentence. This paper
provides a novel approach to this task. We first introduce a neural contextual
utterance encoder featuring three types of self-attention mechanisms. We then
train it using the siamese and triplet energy-based meta-architectures.
Experiments on the AMI corpus show that our system outperforms multiple
energy-based and non-energy based baselines from the state-of-the-art. Code and
data are publicly available.Comment: Update baseline
Multi-step domain adaptation by adversarial attack to -divergence
Adversarial examples are transferable between different models. In our paper,
we propose to use this property for multi-step domain adaptation. In
unsupervised domain adaptation settings, we demonstrate that replacing the
source domain with adversarial examples to -divergence can improve source classifier accuracy on the target
domain. Our method can be connected to most domain adaptation techniques. We
conducted a range of experiments and achieved improvement in accuracy on Digits
and Office-Home datasets
Clustering of illustrations by atmosphere using a combination of supervised and unsupervised learning
The distribution of illustrations on social media, such as Twitter and Pixiv
has increased with the growing popularity of animation, games, and animated
movies. The "atmosphere" of illustrations plays an important role in user
preferences. Classifying illustrations by atmosphere can be helpful for
recommendations and searches. However, assigning clear labels to the elusive
"atmosphere" and conventional supervised classification is not always
practical. Furthermore, even images with similar colors, edges, and low-level
features may not have similar atmospheres, making classification based on
low-level features challenging. In this paper, this problem is solved using
both supervised and unsupervised learning with pseudo-labels. The feature
vectors are obtained using the supervised method with pseudo-labels that
contribute to an ambiguous atmosphere. Further, clustering is performed based
on these feature vectors. Experimental analyses show that our method
outperforms conventional methods in human-like clustering on datasets manually
classified by humans.Comment: 5 pages, 2 figure
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