14,385 research outputs found
Interactive Pattern Recognition applied to Natural Language Processing
This thesis is about Pattern Recognition. In the last decades, huge efforts have been
made to develop automatic systems able to rival human capabilities in this field. Although
these systems achieve high productivity rates, they are not precise enough in
most situations. Humans, on the contrary, are very accurate but comparatively quite
slower. This poses an interesting question: the possibility of benefiting from both
worlds by constructing cooperative systems.
This thesis presents diverse contributions to this kind of collaborative approach.
The point is to improve the Pattern Recognition systems by properly introducing a
human operator into the system. We call this Interactive Pattern Recognition (IPR).
Firstly, a general proposal for IPR will be stated. The aim is to develop a framework
to easily derive new applications in this area. Some interesting IPR issues are
also introduced. Multi-modality or adaptive learning are examples of extensions that
can naturally fit into IPR.
In the second place, we will focus on a specific application. A novel method to
obtain high quality speech transcriptions (CAST, Computer Assisted Speech Transcription).
We will start by proposing a CAST formalization and, next, we will cope
with different implementation alternatives. Practical issues, as the system response
time, will be also taken into account, in order to allow for a practical implementation
of CAST. Word graphs and probabilistic error correcting parsing are tools that will
be used to reach an alternative formulation that allows for the use of CAST in a real
scenario.
Afterwards, a special application within the general IPR framework will be discussed.
This is intended to test the IPR capabilities in an extreme environment, where
no input pattern is available and the system only has access to the user actions to produce
a hypothesis. Specifically, we will focus here on providing assistance in the
problem of text generation.Rodríguez Ruiz, L. (2010). Interactive Pattern Recognition applied to Natural Language Processing [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8479Palanci
Technology-Based Literacy Instruction For English Language Learners
There is a growing need to implement an alternative and viable solution in U.S. K-12 schools that will address the ever-growing gap that the rapidly growing English language learner (ELL) population presents. This article examines various technology-based solutions, and their potential impact. The systematic implementation of these technology-based solutions could aid in alleviating an already taxed educational workforce, as well as significantly aid in promoting English language acquisition among the nation’s K-12 ELL population
Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges
A variety of methods have been applied to the architectural configuration and
learning or training of artificial deep neural networks (DNN). These methods
play a crucial role in the success or failure of the DNN for most problems and
applications. Evolutionary Algorithms (EAs) are gaining momentum as a
computationally feasible method for the automated optimisation and training of
DNNs. Neuroevolution is a term which describes these processes of automated
configuration and training of DNNs using EAs. While many works exist in the
literature, no comprehensive surveys currently exist focusing exclusively on
the strengths and limitations of using neuroevolution approaches in DNNs.
Prolonged absence of such surveys can lead to a disjointed and fragmented field
preventing DNNs researchers potentially adopting neuroevolutionary methods in
their own research, resulting in lost opportunities for improving performance
and wider application within real-world deep learning problems. This paper
presents a comprehensive survey, discussion and evaluation of the
state-of-the-art works on using EAs for architectural configuration and
training of DNNs. Based on this survey, the paper highlights the most pertinent
current issues and challenges in neuroevolution and identifies multiple
promising future research directions.Comment: 20 pages (double column), 2 figures, 3 tables, 157 reference
A proposal for the development of adaptive spoken interfaces to access the Web
Spoken dialog systems have been proposed as a solution to facilitate a more natural human–machine interaction. In this paper, we propose a framework to model the user׳s intention during the dialog and adapt the dialog model dynamically to the user needs and preferences, thus developing more efficient, adapted, and usable spoken dialog systems. Our framework employs statistical models based on neural networks that take into account the history of the dialog up to the current dialog state in order to predict the user׳s intention and the next system response. We describe our proposal and detail its application in the Let׳s Go spoken dialog system.Work partially supported by Projects MINECO TEC2012-37832-
C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/
TIC-1485
On the effective deployment of current machine translation technology
Machine translation is a fundamental technology that is gaining more importance
each day in our multilingual society. Companies and particulars are
turning their attention to machine translation since it dramatically cuts down
their expenses on translation and interpreting. However, the output of current
machine translation systems is still far from the quality of translations generated
by human experts. The overall goal of this thesis is to narrow down
this quality gap by developing new methodologies and tools that improve the
broader and more efficient deployment of machine translation technology.
We start by proposing a new technique to improve the quality of the
translations generated by fully-automatic machine translation systems. The
key insight of our approach is that different translation systems, implementing
different approaches and technologies, can exhibit different strengths and
limitations. Therefore, a proper combination of the outputs of such different
systems has the potential to produce translations of improved quality.
We present minimum Bayes¿ risk system combination, an automatic approach
that detects the best parts of the candidate translations and combines them
to generate a consensus translation that is optimal with respect to a particular
performance metric. We thoroughly describe the formalization of our
approach as a weighted ensemble of probability distributions and provide efficient
algorithms to obtain the optimal consensus translation according to the
widespread BLEU score. Empirical results show that the proposed approach
is indeed able to generate statistically better translations than the provided
candidates. Compared to other state-of-the-art systems combination methods,
our approach reports similar performance not requiring any additional data
but the candidate translations.
Then, we focus our attention on how to improve the utility of automatic
translations for the end-user of the system. Since automatic translations are
not perfect, a desirable feature of machine translation systems is the ability
to predict at run-time the quality of the generated translations. Quality estimation
is usually addressed as a regression problem where a quality score
is predicted from a set of features that represents the translation. However, although the concept of translation quality is intuitively clear, there is no
consensus on which are the features that actually account for it. As a consequence,
quality estimation systems for machine translation have to utilize
a large number of weak features to predict translation quality. This involves
several learning problems related to feature collinearity and ambiguity, and
due to the ¿curse¿ of dimensionality. We address these challenges by adopting
a two-step training methodology. First, a dimensionality reduction method
computes, from the original features, the reduced set of features that better
explains translation quality. Then, a prediction model is built from this
reduced set to finally predict the quality score. We study various reduction
methods previously used in the literature and propose two new ones based on
statistical multivariate analysis techniques. More specifically, the proposed dimensionality
reduction methods are based on partial least squares regression.
The results of a thorough experimentation show that the quality estimation
systems estimated following the proposed two-step methodology obtain better
prediction accuracy that systems estimated using all the original features.
Moreover, one of the proposed dimensionality reduction methods obtained the
best prediction accuracy with only a fraction of the original features. This
feature reduction ratio is important because it implies a dramatic reduction
of the operating times of the quality estimation system.
An alternative use of current machine translation systems is to embed them
within an interactive editing environment where the system and a human expert
collaborate to generate error-free translations. This interactive machine
translation approach have shown to reduce supervision effort of the user in
comparison to the conventional decoupled post-edition approach. However,
interactive machine translation considers the translation system as a passive
agent in the interaction process. In other words, the system only suggests translations
to the user, who then makes the necessary supervision decisions. As
a result, the user is bound to exhaustively supervise every suggested translation.
This passive approach ensures error-free translations but it also demands
a large amount of supervision effort from the user.
Finally, we study different techniques to improve the productivity of current
interactive machine translation systems. Specifically, we focus on the development
of alternative approaches where the system becomes an active agent
in the interaction process. We propose two different active approaches. On the
one hand, we describe an active interaction approach where the system informs
the user about the reliability of the suggested translations. The hope is that
this information may help the user to locate translation errors thus improving
the overall translation productivity. We propose different scores to measure translation reliability at the word and sentence levels and study the influence
of such information in the productivity of an interactive machine translation
system. Empirical results show that the proposed active interaction protocol
is able to achieve a large reduction in supervision effort while still generating
translations of very high quality. On the other hand, we study an active learning
framework for interactive machine translation. In this case, the system is
not only able to inform the user of which suggested translations should be
supervised, but it is also able to learn from the user-supervised translations to
improve its future suggestions. We develop a value-of-information criterion to
select which automatic translations undergo user supervision. However, given
its high computational complexity, in practice we study different selection
strategies that approximate this optimal criterion. Results of a large scale experimentation
show that the proposed active learning framework is able to
obtain better compromises between the quality of the generated translations
and the human effort required to obtain them. Moreover, in comparison to
a conventional interactive machine translation system, our proposal obtained
translations of twice the quality with the same supervision effort.González Rubio, J. (2014). On the effective deployment of current machine translation technology [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37888TESI
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