6,020 research outputs found
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
Adversarial Sampling and Training for Semi-Supervised Information Retrieval
Ad-hoc retrieval models with implicit feedback often have problems, e.g., the
imbalanced classes in the data set. Too few clicked documents may hurt
generalization ability of the models, whereas too many non-clicked documents
may harm effectiveness of the models and efficiency of training. In addition,
recent neural network-based models are vulnerable to adversarial examples due
to the linear nature in them. To solve the problems at the same time, we
propose an adversarial sampling and training framework to learn ad-hoc
retrieval models with implicit feedback. Our key idea is (i) to augment clicked
examples by adversarial training for better generalization and (ii) to obtain
very informational non-clicked examples by adversarial sampling and training.
Experiments are performed on benchmark data sets for common ad-hoc retrieval
tasks such as Web search, item recommendation, and question answering.
Experimental results indicate that the proposed approaches significantly
outperform strong baselines especially for high-ranked documents, and they
outperform IRGAN in NDCG@5 using only 5% of labeled data for the Web search
task.Comment: Published in WWW 201
A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training
The successful application of large pre-trained models such as BERT in
natural language processing has attracted more attention from researchers.
Since the BERT typically acts as an end-to-end black box, classification
systems based on it usually have difficulty in interpretation and low
robustness. This paper proposes a visual interpretation-based self-improving
classification model with a combination of virtual adversarial training (VAT)
and BERT models to address the above problems. Specifically, a fine-tuned BERT
model is used as a classifier to classify the sentiment of the text. Then, the
predicted sentiment classification labels are used as part of the input of
another BERT for spam classification via a semi-supervised training manner
using VAT. Additionally, visualization techniques, including visualizing the
importance of words and normalizing the attention head matrix, are employed to
analyze the relevance of each component to classification accuracy. Moreover,
brand-new features will be found in the visual analysis, and classification
performance will be improved. Experimental results on Twitter's tweet dataset
demonstrate the effectiveness of the proposed model on the classification task.
Furthermore, the ablation study results illustrate the effect of different
components of the proposed model on the classification results
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