20 research outputs found
Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus
Many efforts of research are devoted to semantic role labeling (SRL) which is
crucial for natural language understanding. Supervised approaches have achieved
impressing performances when large-scale corpora are available for
resource-rich languages such as English. While for the low-resource languages
with no annotated SRL dataset, it is still challenging to obtain competitive
performances. Cross-lingual SRL is one promising way to address the problem,
which has achieved great advances with the help of model transferring and
annotation projection. In this paper, we propose a novel alternative based on
corpus translation, constructing high-quality training datasets for the target
languages from the source gold-standard SRL annotations. Experimental results
on Universal Proposition Bank show that the translation-based method is highly
effective, and the automatic pseudo datasets can improve the target-language
SRL performances significantly.Comment: Accepted at ACL 202
Unsupervised Domain Adaptation within Deep Foundation Latent Spaces
The vision transformer-based foundation models, such as ViT or Dino-V2, are
aimed at solving problems with little or no finetuning of features. Using a
setting of prototypical networks, we analyse to what extent such foundation
models can solve unsupervised domain adaptation without finetuning over the
source or target domain. Through quantitative analysis, as well as qualitative
interpretations of decision making, we demonstrate that the suggested method
can improve upon existing baselines, as well as showcase the limitations of
such approach yet to be solved
Multi-source Attention for Unsupervised Domain Adaptation
Domain adaptation considers the problem of generalising a model learnt using data from a particular source domain to a different target domain. Often it is difficult to find a suitable single source to adapt from, and one must consider multiple sources. Using an unrelated source can result in sub-optimal performance, known as the \emph{negative transfer}. However, it is challenging to select the appropriate source(s) for classifying a given target instance in multi-source unsupervised domain adaptation (UDA). We model source-selection as an attention-learning problem, where we learn attention over sources for a given target instance. For this purpose, we first independently learn source-specific classification models, and a relatedness map between sources and target domains using pseudo-labelled target domain instances. Next, we learn attention-weights over the sources for aggregating the predictions of the source-specific models. Experimental results on cross-domain sentiment classification benchmarks show that the proposed method outperforms prior proposals in multi-source UDA
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
Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification
Deep learning-based multi-source unsupervised domain adaptation (MUDA) has
been actively studied in recent years. Compared with single-source unsupervised
domain adaptation (SUDA), domain shift in MUDA exists not only between the
source and target domains but also among multiple source domains. Most existing
MUDA algorithms focus on extracting domain-invariant representations among all
domains whereas the task-specific decision boundaries among classes are largely
neglected. In this paper, we propose an end-to-end trainable network that
exploits domain Consistency Regularization for unsupervised Multi-source domain
Adaptive classification (CRMA). CRMA aligns not only the distributions of each
pair of source and target domains but also that of all domains. For each pair
of source and target domains, we employ an intra-domain consistency to
regularize a pair of domain-specific classifiers to achieve intra-domain
alignment. In addition, we design an inter-domain consistency that targets
joint inter-domain alignment among all domains. To address different
similarities between multiple source domains and the target domain, we design
an authorization strategy that assigns different authorities to domain-specific
classifiers adaptively for optimal pseudo label prediction and self-training.
Extensive experiments show that CRMA tackles unsupervised domain adaptation
effectively under a multi-source setup and achieves superior adaptation
consistently across multiple MUDA datasets
Hallucinating Agnostic Images to Generalize Across Domains
The ability to generalize across visual domains is crucial for the robustness
of artificial recognition systems. Although many training sources may be
available in real contexts, the access to even unlabeled target samples cannot
be taken for granted, which makes standard unsupervised domain adaptation
methods inapplicable in the wild. In this work we investigate how to exploit
multiple sources by hallucinating a deep visual domain composed of images,
possibly unrealistic, able to maintain categorical knowledge while discarding
specific source styles. The produced agnostic images are the result of a deep
architecture that applies pixel adaptation on the original source data guided
by two adversarial domain classifier branches at image and feature level. Our
approach is conceived to learn only from source data, but it seamlessly extends
to the use of unlabeled target samples. Remarkable results for both
multi-source domain adaptation and domain generalization support the power of
hallucinating agnostic images in this framework