716 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
Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning
Typical methods for unsupervised text style transfer often rely on two key
ingredients: 1) seeking the explicit disentanglement of the content and the
attributes, and 2) troublesome adversarial learning. In this paper, we show
that neither of these components is indispensable. We propose a new framework
that utilizes the gradients to revise the sentence in a continuous space during
inference to achieve text style transfer. Our method consists of three key
components: a variational auto-encoder (VAE), some attribute predictors (one
for each attribute), and a content predictor. The VAE and the two types of
predictors enable us to perform gradient-based optimization in the continuous
space, which is mapped from sentences in a discrete space, to find the
representation of a target sentence with the desired attributes and preserved
content. Moreover, the proposed method naturally has the ability to
simultaneously manipulate multiple fine-grained attributes, such as sentence
length and the presence of specific words, when performing text style transfer
tasks. Compared with previous adversarial learning based methods, the proposed
method is more interpretable, controllable and easier to train. Extensive
experimental studies on three popular text style transfer tasks show that the
proposed method significantly outperforms five state-of-the-art methods.Comment: Association for the Advancement of Artificial Intelligence. AAAI 202
Self-Supervised and Controlled Multi-Document Opinion Summarization
We address the problem of unsupervised abstractive summarization of
collections of user generated reviews with self-supervision and control. We
propose a self-supervised setup that considers an individual document as a
target summary for a set of similar documents. This setting makes training
simpler than previous approaches by relying only on standard log-likelihood
loss. We address the problem of hallucinations through the use of control
codes, to steer the generation towards more coherent and relevant
summaries.Finally, we extend the Transformer architecture to allow for multiple
reviews as input. Our benchmarks on two datasets against graph-based and recent
neural abstractive unsupervised models show that our proposed method generates
summaries with a superior quality and relevance.This is confirmed in our human
evaluation which focuses explicitly on the faithfulness of generated summaries
We also provide an ablation study, which shows the importance of the control
setup in controlling hallucinations and achieve high sentiment and topic
alignment of the summaries with the input reviews.Comment: 18 pages including 5 pages appendi
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