Source sentence simplification for statistical machine translation

Long sentences with complex syntax and long-distance dependencies pose difficulties for machine translation systems. Short sentences, on the other hand, are usually easier to translate. We study the potential of addressing this mismatch using text simplifi- cation: given a simplified version of the full input sentence, can we use it in addition to the full input to improve translation? We show that the spaces of original and simplified translations can be effectively combined using translation lattices and compare two decoding approaches to process both inputs at different levels of integration. We demonstrate on source-annotated portions of WMT test sets and on top of strong baseline systems combining hierarchical and neural translation for two language pairs that source simplification can help to improve translation quality.This work was supported by the EPSRC grant Improving Target Language Fluency in Statistical Machine Translation, grant number EP/L027623/1

Can Automatic Post-Editing Make MT More Meaningful?

Automatic post-editors (APEs) enable the re-use of black box machine translation (MT) systems for a variety of tasks where different aspects of translation are important. In this paper, we describe APEs that target adequacy errors, a critical problem for tasks such as cross-lingual question-answering, and compare different approaches for post-editing: a rule-based system and a feedback approach that uses a computer in the loop to suggest improvements to the MT system. We test the APEs on two different MT systems and across two different genres. Human evaluation shows that the APEs significantly improve adequacy, regardless of approach, MT system or genre: 30-56% of the post-edited sentences have improved adequacy compared to the original MT

Simulations of the Microwave Sky and of its ``Observations''

Here follows a preliminary report on the construction of fake millimeter and sub-millimeter skies, as observed by virtual instruments, e.g. the COBRA/SAMBA mission, using theoretical modeling and data extrapolations. Our goal is to create maps as realistic as possible of the relevant physical contributions which may contribute to the detected signals. This astrophysical modeling is followed by simulations of the measurement process itself by a given instrumental configuration. This will enable a precise determination of what can and cannot be achieved with a particular experimental configuration, and provide a feedback on how to improve the overall design. It is a key step on the way to define procedures for the separation of the different physical processes in the future observed maps. Note that this tool will also prove useful in preparing and analyzing current (\eg\ balloon borne) Microwave Background experiments. Keywords: Cosmology -- Microwave Background Anisotropies.Comment: 6 pages of uuencoded compressed postscript (1.2 Mb uncompressed), to appear in the proceedings of the meeting "Far Infrared and Sub-millimeter Space Missions in the Next Decade'', Paris, France, Eds. M. Sauvage, Space Science Revie

Neural Machine Translation by Minimising the Bayes-risk with Respect to Syntactic Translation Lattices

We present a novel scheme to combine neural machine translation (NMT) with traditional statistical machine translation (SMT). Our approach borrows ideas from linearised lattice minimum Bayes-risk decoding for SMT. The NMT score is combined with the Bayes-risk of the translation according the SMT lattice. This makes our approach much more flexible than n-best list or lattice rescoring as the neural decoder is not restricted to the SMT search space. We show an efficient and simple way to integrate risk estimation into the NMT decoder which is suitable for word-level as well as subword-unit-level NMT. We test our method on English-German and Japanese-English and report significant gains over lattice rescoring on several data sets for both single and ensembled NMT. The MBR decoder produces entirely new hypotheses far beyond simply rescoring the SMT search space or fixing UNKs in the NMT output.This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC grant EP/L027623/1)

First Detection of Polarization of the Submillimetre Diffuse Galactic Dust Emission by Archeops

We present the first determination of the Galactic polarized emission at 353 GHz by Archeops. The data were taken during the Arctic night of February 7, 2002 after the balloon--borne instrument was launched by CNES from the Swedish Esrange base near Kiruna. In addition to the 143 GHz and 217 GHz frequency bands dedicated to CMB studies, Archeops had one 545 GHz and six 353 GHz bolometers mounted in three polarization sensitive pairs that were used for Galactic foreground studies. We present maps of the I, Q, U Stokes parameters over 17% of the sky and with a 13 arcmin resolution at 353 GHz (850 microns). They show a significant Galactic large scale polarized emission coherent on the longitude ranges [100, 120] and [180, 200] deg. with a degree of polarization at the level of 4-5%, in agreement with expectations from starlight polarization measurements. Some regions in the Galactic plane (Gem OB1, Cassiopeia) show an even stronger degree of polarization in the range 10-20%. Those findings provide strong evidence for a powerful grain alignment mechanism throughout the interstellar medium and a coherent magnetic field coplanar to the Galactic plane. This magnetic field pervades even some dense clouds. Extrapolated to high Galactic latitude, these results indicate that interstellar dust polarized emission is the major foreground for PLANCK-HFI CMB polarization measurement.Comment: Submitted to Astron. & Astrophys., 14 pages, 12 Fig., 2 Table

Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging

Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated. For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners

Comparative Analysis of Different Definitions of Amyloid-beta Positivity to Detect Early Downstream Pathophysiological Alterations in Preclinical Alzheimer

Amyloid-β (Aβ) positivity is defined using different biomarkers and different criteria. Criteria used in symptomatic patients may conceal meaningful early Aβ pathology in preclinical Alzheimer. Therefore, the description of sensitive cutoffs to study the pathophysiological changes in early stages of the Alzheimer’s continuum is critical. Here, we compare different Aβ classification approaches and we show their performance in detecting pathophysiological changes downstream Aβ pathology. We studied 368 cognitively unimpaired individuals of the ALFA+ study, many of whom in the preclinical stage of the Alzheimer’s continuum. Participants underwent Aβ PET and CSF biomarkers assessment. We classified participants as Aβ -positive using five approaches: (1) CSF Aβ42 12; (4) Aβ PET Centiloid > 30 or (5) Aβ PET Positive visual read. We assessed the correlations between Aβ biomarkers and compared the prevalence of Aβ positivity. We determined which approach significantly detected associations between Aβ pathology and tau/neurodegeneration CSF biomarkers. We found that CSF-based approaches result in a higher Aβ-positive prevalence than PET-based ones. There was a higher number of discordant participants classified as CSF Aβ-positive but PET Aβ-negative than CSF Aβ-negative but PET Aβ-positive. The CSF Aβ 42/40 approach allowed optimal detection of significant associations with CSF p-tau and t-tau in the Aβ-positive group. Altogether, we highlight the need for sensitive Aβ -classifications to study the preclinical Alzheimer’s continuum. Approaches that define Aβ positivity based on optimal discrimination of symptomatic Alzheimer’s disease patients may be suboptimal for the detection of early pathophysiological alterations in preclinical Alzheimer

Association of weight change with cerebrospinal fluid biomarkers and amyloid positron emission tomography in preclinical Alzheimer's disease

BACKGROUND: Recognizing clinical manifestations heralding the development of Alzheimer's disease (AD)-related cognitive impairment could improve the identification of individuals at higher risk of AD who may benefit from potential prevention strategies targeting preclinical population. We aim to characterize the association of body weight change with cognitive changes and AD biomarkers in cognitively unimpaired middle-aged adults. METHODS: This prospective cohort study included data from cognitively unimpaired adults from the ALFA study (n = 2743), a research platform focused on preclinical AD. Cognitive and anthropometric data were collected at baseline between April 2013 and November 2014. Between October 2016 and February 2020, 450 participants were visited in the context of the nested ALFA+ study and underwent cerebrospinal fluid (CSF) extraction and acquisition of positron emission tomography images with [18F]flutemetamol (FTM-PET). From these, 408 (90.1%) were included in the present study. We used data from two visits (average interval 4.1 years) to compute rates of change in weight and cognitive performance. We tested associations between these variables and between weight change and categorical and continuous measures of CSF and neuroimaging AD biomarkers obtained at follow-up. We classified participants with CSF data according to the AT (amyloid, tau) system and assessed between-group differences in weight change. RESULTS: Weight loss predicted a higher likelihood of positive FTM-PET visual read (OR 1.27, 95% CI 1.00-1.61, p = 0.049), abnormal CSF p-tau levels (OR 1.50, 95% CI 1.19-1.89, p = 0.001), and an A+T+ profile (OR 1.64, 95% CI 1.25-2.20, p = 0.001) and was greater among participants with an A+T+ profile (p < 0.01) at follow-up. Weight change was positively associated with CSF Aβ42/40 ratio (β = 0.099, p = 0.032) and negatively associated with CSF p-tau (β = - 0.141, p = 0.005), t-tau (β = - 0.147 p = 0.004) and neurogranin levels (β = - 0.158, p = 0.002). In stratified analyses, weight loss was significantly associated with higher t-tau, p-tau, neurofilament light, and neurogranin, as well as faster cognitive decline in A+ participants only. CONCLUSIONS: Weight loss predicts AD CSF and PET biomarker results and may occur downstream to amyloid-β accumulation in preclinical AD, paralleling cognitive decline. Accordingly, it should be considered as an indicator of increased risk of AD-related cognitive impairment. TRIAL REGISTRATION: NCT01835717 , NCT02485730 , NCT02685969