5 research outputs found
Domain-Adversarial Training of Neural Networks
We introduce a new representation learning approach for domain adaptation, in
which data at training and test time come from similar but different
distributions. Our approach is directly inspired by the theory on domain
adaptation suggesting that, for effective domain transfer to be achieved,
predictions must be made based on features that cannot discriminate between the
training (source) and test (target) domains. The approach implements this idea
in the context of neural network architectures that are trained on labeled data
from the source domain and unlabeled data from the target domain (no labeled
target-domain data is necessary). As the training progresses, the approach
promotes the emergence of features that are (i) discriminative for the main
learning task on the source domain and (ii) indiscriminate with respect to the
shift between the domains. We show that this adaptation behaviour can be
achieved in almost any feed-forward model by augmenting it with few standard
layers and a new gradient reversal layer. The resulting augmented architecture
can be trained using standard backpropagation and stochastic gradient descent,
and can thus be implemented with little effort using any of the deep learning
packages. We demonstrate the success of our approach for two distinct
classification problems (document sentiment analysis and image classification),
where state-of-the-art domain adaptation performance on standard benchmarks is
achieved. We also validate the approach for descriptor learning task in the
context of person re-identification application.Comment: Published in JMLR: http://jmlr.org/papers/v17/15-239.htm
Automatic detection and attribution of quotes
Quotations extraction and attribution are important practical tasks for the media, but most of the presented solutions are monolingual. In this work, I present a complex machine learning-based system for extraction and attribution of direct and indirect quo- tations, which is trained on English and tested on Czech and Russian data. Czech and Russian test datasets were manually annotated as part of this study. This system is com- pared against a rule-based baseline model. Baseline model demonstrates better precision in extraction of quotation elements, but low recall. The machine learning-based model is better overall in extracting separate elements of quotations and full quotations as well.
Automatická identifikace citátů
Quotations extraction and attribution are important practical tasks for the media, but most of the presented solutions are monolingual. In this work, I present a complex machine learning-based system for extraction and attribution of direct and indirect quo- tations, which is trained on English and tested on Czech and Russian data. Czech and Russian test datasets were manually annotated as part of this study. This system is com- pared against a rule-based baseline model. Baseline model demonstrates better precision in extraction of quotation elements, but low recall. The machine learning-based model is better overall in extracting separate elements of quotations and full quotations as well. 1Ústav formální a aplikované lingvistikyInstitute of Formal and Applied LinguisticsFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult