83,617 research outputs found
Active learning for interactive machine translation
Translation needs have greatly increased
during the last years. In many situations, text to be translated constitutes an
unbounded stream of data that grows continually with time. An effective approach
to translate text documents is to follow
an interactive-predictive paradigm in which
both the system is guided by the user
and the user is assisted by the system to
generate error-free translations. Unfortunately, when processing such unbounded
data streams even this approach requires an
overwhelming amount of manpower. Is in
this scenario where the use of active learning techniques is compelling. In this work,
we propose different active learning techniques for interactive machine translation.
Results show that for a given translation
quality the use of active learning allows us
to greatly reduce the human effort required
to translate the sentences in the stream.González Rubio, J.; Ortiz Martínez, D.; Casacuberta Nolla, F. (2012). Active learning for interactive machine translation. En Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics. 245-254. http://hdl.handle.net/10251/1639524525
Cost-sensitive active learning for computer-assisted translation
This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, [Volume 37, 1 February 2014, Pages 124–134] DOI: 10.1016/j.patrec.2013.06.007[EN] Machine translation technology is not perfect. To be successfully embedded in real-world applications, it must compensate for its imperfections by interacting intelligently with the user within a computer-assisted translation framework. The interactive¿predictive paradigm, where both a statistical translation model and a human expert collaborate to generate the translation, has been shown to be an effective computer-assisted translation approach. However, the exhaustive supervision of all translations and the use of non-incremental translation models penalizes the productivity of conventional interactive¿predictive systems.
We propose a cost-sensitive active learning framework for computer-assisted translation whose goal is to make the translation process as painless as possible. In contrast to conventional active learning scenarios, the proposed active learning framework is designed to minimize not only how many translations the user must supervise but also how difficult each translation is to supervise. To do that, we address the two potential drawbacks of the interactive-predictive translation paradigm. On the one hand, user effort is focused to those translations whose user supervision is considered more ¿informative¿, thus, maximizing the utility of each user interaction. On the other hand, we use a dynamic machine translation model that is continually updated with user feedback after deployment. We empirically validated each of the technical components in simulation and quantify the user effort saved. We conclude that both selective translation supervision and translation model updating lead to important user-effort reductions, and consequently to improved translation productivity.Work supported by the European Union Seventh Framework Program (FP7/2007-2013) under the CasMaCat Project (Grants agreement No. 287576), by the Generalitat Valenciana under Grant ALMPR (Prometeo/2009/014), and by the Spanish Government under Grant TIN2012-31723. The authors thank Daniel Ortiz-Martinez for providing us with the log-linear SMT model with incremental features and the corresponding online learning algorithms. The authors also thank the anonymous reviewers for their criticisms and suggestions.González Rubio, J.; Casacuberta Nolla, F. (2014). Cost-sensitive active learning for computer-assisted translation. Pattern Recognition Letters. 37(1):124-134. https://doi.org/10.1016/j.patrec.2013.06.007S12413437
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
Reinforcement Learning for Machine Translation: from Simulations to Real-World Applications
If a machine translation is wrong, how we can tell the underlying model to fix it? Answering this question requires (1) a machine learning algorithm to define update rules, (2) an interface for feedback to be submitted, and (3) expertise on the side of the human who gives the feedback. This thesis investigates solutions for machine learning updates, the suitability of feedback interfaces, and the dependency on reliability and expertise for different types of feedback.
We start with an interactive online learning scenario where a machine translation (MT) system receives bandit feedback (i.e. only once per source) instead of references for learning. Policy gradient algorithms for statistical and neural MT are developed to learn from absolute and pairwise judgments. Our experiments on domain adaptation with simulated online feedback show that the models can largely improve under weak feedback, with variance reduction techniques being very effective.
In production environments offline learning is often preferred over online learning. We evaluate algorithms for counterfactual learning from human feedback in a study on eBay product title translations. Feedback is either collected via explicit star ratings from users, or implicitly from the user interaction with cross-lingual product search. Leveraging implicit feedback turns out to be more successful due to lower levels of noise. We compare the reliability and learnability of absolute Likert-scale ratings with pairwise preferences in a smaller user study, and find that absolute ratings are overall more effective for improvements in down-stream tasks. Furthermore, we discover that error markings provide a cheap and practical alternative to error corrections.
In a generalized interactive learning framework we propose a self-regulation approach, where the learner, guided by a regulator module, decides which type of feedback to choose for each input. The regulator is reinforced to find a good trade-off between supervision effect and cost. In our experiments, it discovers strategies that are more efficient than active learning and standard fully supervised learning
Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation
In interactive machine translation (MT),
human translators correct errors in auto-
matic translations in collaboration with the
MT systems, which is seen as an effective
way to improve the productivity gain in
translation. In this study, we model source-
language syntactic constituency parse and
target-language syntactic descriptions in
the form of supertags as conditional con-
text for interactive prediction in neural
MT (NMT). We found that the supertags
significantly improve productivity gain in
translation in interactive-predictive NMT
(INMT), while syntactic parsing somewhat
found to be effective in reducing human
efforts in translation. Furthermore, when
we model this source- and target-language
syntactic information together as the con-
ditional context, both types complement
each other and our fully syntax-informed
INMT model shows statistically significant
reduction in human efforts for a French–
to–English translation task in a reference-
simulated setting, achieving 4.30 points
absolute (corresponding to 9.18% relative)
improvement in terms of word prediction
accuracy (WPA) and 4.84 points absolute
(corresponding to 9.01% relative) reduc-
tion in terms of word stroke ratio (WSR)
over the baseline
A Neural, Interactive-predictive System for Multimodal Sequence to Sequence Tasks
We present a demonstration of a neural interactive-predictive system for
tackling multimodal sequence to sequence tasks. The system generates text
predictions to different sequence to sequence tasks: machine translation, image
and video captioning. These predictions are revised by a human agent, who
introduces corrections in the form of characters. The system reacts to each
correction, providing alternative hypotheses, compelling with the feedback
provided by the user. The final objective is to reduce the human effort
required during this correction process.
This system is implemented following a client-server architecture. For
accessing the system, we developed a website, which communicates with the
neural model, hosted in a local server. From this website, the different tasks
can be tackled following the interactive-predictive framework. We open-source
all the code developed for building this system. The demonstration in hosted in
http://casmacat.prhlt.upv.es/interactive-seq2seq.Comment: ACL 2019 - System demonstration
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