2,384 research outputs found
Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution
Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding
Proceedings of the 17th Annual Conference of the European Association for Machine Translation
Proceedings of the 17th Annual Conference of the European Association for Machine Translation (EAMT
Template update algorithms and their application to face recognition systems in the deep learning era
Biometric technologies and facial recognition systems are reaching a very high diffusion for authentication in personal devices and public and private security systems, thanks to their intrinsic reliability and user-friendliness. However, although deep learning-based facial features reached a significant level of compactness and expressive power, the facial recognition performance still suffers from intra-class variations such as ageing, different facial expressions, different poses and lighting changes.
In the last decade, several "adaptive" biometric systems have been proposed to deal with this problem. Unfortunately, adaptive methods usually lead to a growth of the system in terms of memory and computational complexity and involve the risk of inserting impostors among the templates.
The first goal of this PhD thesis is the presentation of a novel template-based self-update algorithm, able to keep over time the expressive power of a limited set of templates.
This classification-selection approach overcomes the problem of manual updating and stringent computational requirements.
In the second part of the thesis, we analyzed if and to what extent this "optimized" self-updating strategy improves the facial recognition performance, especially in application contexts where the facial biometric trait undergoes great changes due to the passage of time. In contexts of long-term use, in fact, the high representativeness of the deep features may not be enough and this is usually overcome with a re-enrollment phase.
For this reason, one of our goals was to evaluate how much an automatic template updating system could compete with human-in-the-loop in terms of performance.
To simulate situations of long-term use in which the temporal variability of biometric data is high, we acquired a new dataset collected by using frames of some videos in YouTube related to Daily Photo Projects: people take a picture every day for a certain period of time, usually to show how
their appearance is changing.
The temporal information present in this new dataset allowed us to evaluate how long a facial feature can remain representative depending on the context and the recognition system.
Extensive experiments on different datasets and using different facial features are conducted to define the contexts of applicability and the usefulness of adaptive systems in the deep learning era
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
Perspectives on automated composition of workflows in the life sciences [version 1; peer review: 2 approved]
Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus.
This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years.
A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.Stian Soiland-Reyes was supported by BioExcel-2 Centre of Excellence, funded by European Commission Horizon
2020 programme under European Commission contract H2020-INFRAEDI-02-2018 823830. Carole Goble was supported by EOSC-Life,
funded by European Commission Horizon 2020 programme under grant agreement H2020-INFRAEOSC-2018-2 824087. We gratefully
acknowledge the financial support from the Lorentz Center, ELIXIR, and the Leiden University Medical Center (LUMC) that made the
workshop possible.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscriptPeer Reviewed"Article signat per 33 autors/es: Anna-Lena Lamprecht , Magnus Palmblad, Jon Ison, Veit Schwämmle , Mohammad Sadnan Al Manir, Ilkay Altintas, Christopher J. O. Baker, Ammar Ben Hadj Amor, Salvador Capella-Gutierrez, Paulos Charonyktakis, Michael R. Crusoe, Yolanda Gil, Carole Goble, Timothy J. Griffin , Paul Groth , Hans Ienasescu, Pratik Jagtap, Matúš Kalaš , Vedran Kasalica, Alireza Khanteymoori , Tobias Kuhn12, Hailiang Mei, Hervé Ménager, Steffen Möller, Robin A. Richardson, Vincent Robert9, Stian Soiland-Reyes, Robert Stevens, Szoke Szaniszlo, Suzan Verberne, Aswin Verhoeven, Katherine Wolstencroft "Postprint (published version
Coherence in Machine Translation
Coherence ensures individual sentences work together to form a meaningful document. When properly translated, a coherent document in one language should result in a coherent document in another language. In Machine Translation, however, due to reasons of modeling and computational complexity, sentences are pieced together from words or phrases based on short context windows and
with no access to extra-sentential context.
In this thesis I propose ways to automatically assess the coherence of machine translation output. The work is structured around three dimensions: entity-based coherence, coherence as evidenced via syntactic patterns, and coherence as
evidenced via discourse relations.
For the first time, I evaluate existing monolingual coherence models on this new task, identifying issues and challenges that are specific to the machine translation setting. In order to address these issues, I adapted a state-of-the-art syntax
model, which also resulted in improved performance for the monolingual task. The results clearly indicate how much more difficult the new task is than the task of detecting shuffled texts. I proposed a new coherence model, exploring the crosslingual transfer of discourse relations in machine translation. This model is novel in that it measures the correctness of the discourse relation by comparison to the source text rather than to a reference translation. I identified patterns of incoherence common across different language pairs, and created a corpus of machine translated output annotated with coherence errors for evaluation purposes. I then examined
lexical coherence in a multilingual context, as a preliminary study for crosslingual transfer. Finally, I determine how the new and adapted models correlate with human judgements of translation quality and suggest that improvements in general evaluation within machine translation would benefit from having a coherence component that evaluated the translation output with respect to the source text
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