5,369 research outputs found
Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces
We combine multi-task learning and semi-supervised learning by inducing a
joint embedding space between disparate label spaces and learning transfer
functions between label embeddings, enabling us to jointly leverage unlabelled
data and auxiliary, annotated datasets. We evaluate our approach on a variety
of sequence classification tasks with disparate label spaces. We outperform
strong single and multi-task baselines and achieve a new state-of-the-art for
topic-based sentiment analysis.Comment: To appear at NAACL 2018 (long
Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification
We consider the cross-domain sentiment classification problem, where a
sentiment classifier is to be learned from a source domain and to be
generalized to a target domain. Our approach explicitly minimizes the distance
between the source and the target instances in an embedded feature space. With
the difference between source and target minimized, we then exploit additional
information from the target domain by consolidating the idea of semi-supervised
learning, for which, we jointly employ two regularizations -- entropy
minimization and self-ensemble bootstrapping -- to incorporate the unlabeled
target data for classifier refinement. Our experimental results demonstrate
that the proposed approach can better leverage unlabeled data from the target
domain and achieve substantial improvements over baseline methods in various
experimental settings.Comment: Accepted to EMNLP201
Graph Few-shot Learning via Knowledge Transfer
Towards the challenging problem of semi-supervised node classification, there
have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have
aroused great interest recently, which update the representation of each node
by aggregating information of its neighbors. However, most GNNs have shallow
layers with a limited receptive field and may not achieve satisfactory
performance especially when the number of labeled nodes is quite small. To
address this challenge, we innovatively propose a graph few-shot learning (GFL)
algorithm that incorporates prior knowledge learned from auxiliary graphs to
improve classification accuracy on the target graph. Specifically, a
transferable metric space characterized by a node embedding and a
graph-specific prototype embedding function is shared between auxiliary graphs
and the target, facilitating the transfer of structural knowledge. Extensive
experiments and ablation studies on four real-world graph datasets demonstrate
the effectiveness of our proposed model.Comment: Full paper (with Appendix) of AAAI 202
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
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