1,792 research outputs found
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
In this paper, we propose a novel deep learning architecture for multi-label
zero-shot learning (ML-ZSL), which is able to predict multiple unseen class
labels for each input instance. Inspired by the way humans utilize semantic
knowledge between objects of interests, we propose a framework that
incorporates knowledge graphs for describing the relationships between multiple
labels. Our model learns an information propagation mechanism from the semantic
label space, which can be applied to model the interdependencies between seen
and unseen class labels. With such investigation of structured knowledge graphs
for visual reasoning, we show that our model can be applied for solving
multi-label classification and ML-ZSL tasks. Compared to state-of-the-art
approaches, comparable or improved performances can be achieved by our method.Comment: CVPR 201
Learning Deep Latent Spaces for Multi-Label Classification
Multi-label classification is a practical yet challenging task in machine
learning related fields, since it requires the prediction of more than one
label category for each input instance. We propose a novel deep neural networks
(DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this
task. Aiming at better relating feature and label domain data for improved
classification, we uniquely perform joint feature and label embedding by
deriving a deep latent space, followed by the introduction of label-correlation
sensitive loss function for recovering the predicted label outputs. Our C2AE is
achieved by integrating the DNN architectures of canonical correlation analysis
and autoencoder, which allows end-to-end learning and prediction with the
ability to exploit label dependency. Moreover, our C2AE can be easily extended
to address the learning problem with missing labels. Our experiments on
multiple datasets with different scales confirm the effectiveness and
robustness of our proposed method, which is shown to perform favorably against
state-of-the-art methods for multi-label classification.Comment: published in AAAI-201
Self-Augmentation Improves Zero-Shot Cross-Lingual Transfer
Zero-shot cross-lingual transfer is a central task in multilingual NLP,
allowing models trained in languages with more sufficient training resources to
generalize to other low-resource languages. Earlier efforts on this task use
parallel corpora, bilingual dictionaries, or other annotated alignment data to
improve cross-lingual transferability, which are typically expensive to obtain.
In this paper, we propose a simple yet effective method, SALT, to improve the
zero-shot cross-lingual transfer of the multilingual pretrained language models
without the help of such external data. By incorporating code-switching and
embedding mixup with self-augmentation, SALT effectively distills cross-lingual
knowledge from the multilingual PLM and enhances its transferability on
downstream tasks. Experimental results on XNLI and PAWS-X show that our method
is able to improve zero-shot cross-lingual transferability without external
data. Our code is available at https://github.com/luka-group/SALT.Comment: AACL 202
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