7,205 research outputs found
Qualitative analysis of post-editing for high quality machine translation
In the context of massive adoption of Machine Translation (MT) by human localization services in Post-Editing (PE) workflows, we analyze the activity of post-editing high quality translations through a novel PE analysis methodology. We define and introduce a new unit for evaluating post-editing effort based on Post-Editing Action (PEA) - for which we provide human evaluation guidelines and propose a process to automatically evaluate these PEAs. We applied this methodology on data sets from two technologically different MT systems. In that context, we could show that more than 35% of the remaining effort can be
saved by introducing of global PEA and edit propagation
Semantic Parsing in Limited Resource Conditions
This thesis explores challenges in semantic parsing, specifically focusing on
scenarios with limited data and computational resources. It offers solutions
using techniques like automatic data curation, knowledge transfer, active
learning, and continual learning.
For tasks with no parallel training data, the thesis proposes generating
synthetic training examples from structured database schemas. When there is
abundant data in a source domain but limited parallel data in a target domain,
knowledge from the source is leveraged to improve parsing in the target domain.
For multilingual situations with limited data in the target languages, the
thesis introduces a method to adapt parsers using a limited human translation
budget. Active learning is applied to select source-language samples for manual
translation, maximizing parser performance in the target language. In addition,
an alternative method is also proposed to utilize machine translation services,
supplemented by human-translated data, to train a more effective parser.
When computational resources are limited, a continual learning approach is
introduced to minimize training time and computational memory. This maintains
the parser's efficiency in previously learned tasks while adapting it to new
tasks, mitigating the problem of catastrophic forgetting.
Overall, the thesis provides a comprehensive set of methods to improve
semantic parsing in resource-constrained conditions.Comment: PhD thesis, year of award 2023, 172 page
Multi-Content GAN for Few-Shot Font Style Transfer
In this work, we focus on the challenge of taking partial observations of
highly-stylized text and generalizing the observations to generate unobserved
glyphs in the ornamented typeface. To generate a set of multi-content images
following a consistent style from very few examples, we propose an end-to-end
stacked conditional GAN model considering content along channels and style
along network layers. Our proposed network transfers the style of given glyphs
to the contents of unseen ones, capturing highly stylized fonts found in the
real-world such as those on movie posters or infographics. We seek to transfer
both the typographic stylization (ex. serifs and ears) as well as the textual
stylization (ex. color gradients and effects.) We base our experiments on our
collected data set including 10,000 fonts with different styles and demonstrate
effective generalization from a very small number of observed glyphs
Learning-Assisted Automated Reasoning with Flyspeck
The considerable mathematical knowledge encoded by the Flyspeck project is
combined with external automated theorem provers (ATPs) and machine-learning
premise selection methods trained on the proofs, producing an AI system capable
of answering a wide range of mathematical queries automatically. The
performance of this architecture is evaluated in a bootstrapping scenario
emulating the development of Flyspeck from axioms to the last theorem, each
time using only the previous theorems and proofs. It is shown that 39% of the
14185 theorems could be proved in a push-button mode (without any high-level
advice and user interaction) in 30 seconds of real time on a fourteen-CPU
workstation. The necessary work involves: (i) an implementation of sound
translations of the HOL Light logic to ATP formalisms: untyped first-order,
polymorphic typed first-order, and typed higher-order, (ii) export of the
dependency information from HOL Light and ATP proofs for the machine learners,
and (iii) choice of suitable representations and methods for learning from
previous proofs, and their integration as advisors with HOL Light. This work is
described and discussed here, and an initial analysis of the body of proofs
that were found fully automatically is provided
Data-efficient deep representation learning
Current deep learning methods succeed in many data-intensive applications, but they are still not able to produce robust performance due to the lack of training samples. To investigate how to improve the performance of deep learning paradigms when training samples are limited, data-efficient deep representation learning (DDRL) is proposed in this study. DDRL as a sub area of representation learning mainly addresses the following problem: How can the performance of a deep learning method be maintained when the number of training samples is significantly reduced? This is vital for many applications where collecting data is highly costly, such as medical image analysis. Incorporating a certain kind of prior knowledge into the learning paradigm is key to achieving data efficiency.
Deep learning as a sub-area of machine learning can be divided into three parts (locations) in its learning process, namely Data, Optimisation and Model. Integrating prior knowledge into these three locations is expected to bring data efficiency into a learning paradigm, which can dramatically increase the model performance under the condition of limited training data.
In this thesis, we aim to develop novel deep learning methods for achieving data-efficient training, each of which integrates a certain kind of prior knowledge into three different locations respectively. We make the following contributions. First, we propose an iterative solution based on deep learning for medical image segmentation tasks, where dynamical systems are integrated into the segmentation labels in order to improve both performance and data efficiency. The proposed method not only shows a superior performance and better data efficiency compared to the state-of-the-art methods, but also has better interpretability and rotational invariance which are desired for medical imagining applications. Second, we propose a novel training framework which adaptively selects more informative samples for training during the optimization process. The adaptive selection or sampling is performed based on a hardness-aware strategy in the latent space constructed by a generative model.
We show that the proposed framework outperforms a random sampling method, which demonstrates effectiveness of the proposed framework. Thirdly, we propose a deep neural network model which produces the segmentation maps in a coarse-to-fine manner. The proposed architecture is a sequence of computational blocks containing a number of convolutional layers in which each block provides its successive block with a coarser segmentation map as a reference. Such mechanisms enable us to train the network with limited training samples and produce more interpretable results.Open Acces
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