3,784 research outputs found
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 complex path around the sign problem
We review recent attempts at dealing with the sign problem in Monte Carlo
calculations by deforming the region of integration in the path integral from
real to complex fields. We discuss the theoretical foundations, the algorithmic
issues and present some results for low dimensional field theories in both
imaginary and real time.Comment: Write up of the talk delivered al Lattice 201
Stochastic integrals and conditional full support
We present conditions that imply the conditional full support (CFS) property,
introduced by Guasoni, R\'asonyi, and Schachermayer [Ann. Appl. Probab., 18
(2008), pp. 491--520], for processes Z := H + K \cdot W, where W is a Brownian
motion, H is a continuous process, and processes H and K are either progressive
or independent of W. Moreover, in the latter case under an additional
assumption that K is of finite variation, we present conditions under which Z
has CFS also when W is replaced with a general continuous process with CFS. As
applications of these results, we show that several stochastic volatility
models and the solutions of certain stochastic differential equations have CFS.Comment: 19 pages, v3: almost entirely rewritten, new result
Compact convex sets of the plane and probability theory
The Gauss-Minkowski correspondence in states the existence of
a homeomorphism between the probability measures on such that
and the compact convex sets (CCS) of the plane
with perimeter~1. In this article, we bring out explicit formulas relating the
border of a CCS to its probability measure. As a consequence, we show that some
natural operations on CCS -- for example, the Minkowski sum -- have natural
translations in terms of probability measure operations, and reciprocally, the
convolution of measures translates into a new notion of convolution of CCS.
Additionally, we give a proof that a polygonal curve associated with a sample
of random variables (satisfying ) converges
to a CCS associated with at speed , a result much similar to
the convergence of the empirical process in statistics. Finally, we employ this
correspondence to present models of smooth random CCS and simulations
Interest rate volatility and alternative monetary control procedure
Monetary policy - United States ; Interest rates ; Federal Open Market Committee
TermEval: an automatic metric for evaluating terminology translation in MT
Terminology translation plays a crucial role in domain-specific machine translation (MT). Preservation of domain-knowledge from source to target is arguably the most concerning factor for the customers in translation industry, especially for critical domains such as medical, transportation, military, legal and aerospace. However, evaluation of terminology translation, despite its huge importance in the translation industry, has been a less examined area in MT research. Term translation quality in MT is usually measured with domain experts, either in academia or industry. To the best of our knowledge, as of yet there is no publicly available solution to automatically evaluate terminology translation in MT. In particular, manual intervention is often needed to evaluate terminology translation in MT, which, by nature, is a time-consuming and highly expensive task. In fact, this is unimaginable in an industrial setting where customised MT systems are often needed to be updated for many reasons (e.g. availability of new training data or leading MT techniques). Hence, there is a genuine need to have a faster and less expensive solution to this problem,
which could aid the end-users to instantly identify term translation problems in MT.
In this study, we propose an automatic evaluation metric, TermEval, for evaluating terminology translation in MT. To the best of our knowledge, there is no gold-standard dataset available for measuring terminology translation quality in MT. In the absence of gold standard evaluation test set, we semi-automatically create a gold-standard dataset from English--Hindi judicial domain parallel corpus.
We trained state-of-the-art phrase-based SMT (PB-SMT) and neural MT (NMT) models on two translation directions: English-to-Hindi and Hindi-to-English, and use TermEval to evaluate their performance on terminology translation over the created gold standard test set. In order to measure the correlation between TermEval scores and human judgments, translations of each source terms (of the gold standard test set) is validated with human evaluator. High correlation between TermEval and human judgements manifests the effectiveness of the proposed terminology translation evaluation metric. We also carry out comprehensive manual evaluation on terminology translation and present our observations
Online Learning for Effort Reduction in Interactive Neural Machine Translation
[EN] Neural machine translation systems require large amounts of training data and resources. Even with this, the quality of the translations may be insufficient for some users or domains. In such cases, the output of the system must be revised by a human agent. This can be done in a post-editing stage or following an interactive machine translation protocol.
We explore the incremental update of neural machine translation systems during the post-editing or interactive translation processes. Such modifications aim to incorporate the new knowledge, from the edited sentences, into the translation system. Updates to the model are performed on-the-fly, as sentences are corrected, via online learning techniques. In addition, we implement a novel interactive, adaptive system, able to react to single-character interactions. This system greatly reduces the human effort required for obtaining high-quality translations.
In order to stress our proposals, we conduct exhaustive experiments varying the amount and type of data available for training. Results show that online learning effectively achieves the objective of reducing the human effort required during the post-editing or the interactive machine translation stages. Moreover, these adaptive systems also perform well in scenarios with scarce resources. We show that a neural machine translation system can be rapidly adapted to a specific domain, exclusively by means of online learning techniques.The authors wish to thank the anonymous reviewers for their valuable criticisms and suggestions. The research
leading to these results has received funding from the Generalitat Valenciana under grant PROMETEOII/2014/030
and from TIN2015-70924-C2-1-R. We also acknowledge NVIDIA Corporation for the donation of GPUs used in this work.Peris-Abril, Á.; Casacuberta Nolla, F. (2019). Online Learning for Effort Reduction in Interactive Neural Machine Translation. Computer Speech & Language. 58:98-126. https://doi.org/10.1016/j.csl.2019.04.001S981265
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