Multi-platform image search using tag enrichment

The number of images available online is growing steadily and current web search engines have indexed more than 10 billion images. Approaches to image retrieval are still often text-based and operate on image annotations and captions. Image annotations (i.e. image tags) are typically short, user-generated, and of varying quality, which increases the mismatch problem between query terms and image tags. For example, a user might enter the query wedding dress while all images are annotated with bridal gown or wedding gown. This demonstration presents an image search system using reduction and expansion of image annotations to overcome vocabulary mismatch problems by enriching the sparse set of image tags

When less is more in neural quality estimation of machine translation. An industry case study

Quality estimation (QE) of machine translation (MT), the task of predicting the quality of an MT output without human references, is particularly suitable in dynamic translation workflows, where translations need to be assessed continuously with no specific reference provided. In this paper, we investigate sentence-level neural QE and its applicability in an industry use case. We assess six QE approaches, which we divide into two-phase and one-phase approaches, based on quality and cost. Our evaluation shows that while two-phase systems perform best in terms of the predicted QE scores, their computational costs suggest that alternatives should be considered for large-scale translation production

When less is more in neural quality estimation of machine translation. An industry case study

Quality estimation (QE) of machine translation (MT), the task of predicting the quality of an MT output without human references, is particularly suitable in dynamic translation workflows, where translations need to be assessed continuously with no specific reference provided. In this paper, we investigate sentence-level neural QE and its applicability in an industry use case. We assess six QE approaches, which we divide into two-phase and one-phase approaches, based on quality and cost. Our evaluation shows that while two-phase systems perform best in terms of the predicted QE scores, their computational costs suggest that alternatives should be considered for large-scale translation production

A roadmap to neural automatic post-editing: an empirical approach

In a translation workflow, machine translation (MT) is almost always followed by a human post-editing step, where the raw MT output is corrected to meet required quality standards. To reduce the number of errors human translators need to correct, automatic post-editing (APE) methods have been developed and deployed in such workflows. With the advances in deep learning, neural APE (NPE) systems have outranked more traditional, statistical, ones. However, the plethora of options, variables and settings, as well as the relation between NPE performance and train/test data makes it difficult to select the most suitable approach for a given use case. In this article, we systematically analyse these different parameters with respect to NPE performance. We build an NPE “roadmap” to trace the different decision points and train a set of systems selecting different options through the roadmap. We also propose a novel approach for APE with data augmentation. We then analyse the performance of 15 of these systems and identify the best ones. In fact, the best systems are the ones that follow the newly-proposed method. The work presented in this article follows from a collaborative project between Microsoft and the ADAPT centre. The data provided by Microsoft originates from phrase-based statistical MT (PBSMT) systems employed in production. All tested NPE systems significantly increase the translation quality, proving the effectiveness of neural post-editing in the context of a commercial translation workflow that leverages PBSMT

A review of the state‑of‑the‑art in automatic post‑editing

This article presents a review of the evolution of automatic post-editing, a term that describes methods to improve the output of machine translation systems, based on knowledge extracted from datasets that include post-edited content. The article describes the specificity of automatic post-editing in comparison with other tasks in machine translation, and it discusses how it may function as a complement to them. Particular detail is given in the article to the five-year period that covers the shared tasks presented in WMT conferences (2015–2019). In this period, discussion of automatic post-editing evolved from the definition of its main parameters to an announced demise, associated with the difficulties in improving output obtained by neural methods, which was then followed by renewed interest. The article debates the role and relevance of automatic post-editing, both as an academic endeavour and as a useful application in commercial workflows

Nanotechnology in Glycomics: Applications in Diagnostics, Therapy, Imaging, and Separation Processes

This review comprehensively covers the most recent achievements (from 2013) in the successful integration of nanomaterials in the field of glycomics. The first part of the paper addresses the beneficial properties of nanomaterials for the construction of biosensors, bioanalytical devices, and protocols for the detection of various analytes, including viruses and whole cells, together with their key characteristics. The second part of the review focuses on the application of nanomaterials integrated with glycans for various biomedical applications, that is, vaccines against viral and bacterial infections and cancer cells, as therapeutic agents, for in vivo imaging and nuclear magnetic resonance imaging, and for selective drug delivery. The final part of the review describes various ways in which glycan enrichment can be effectively done using nanomaterials, molecularly imprinted polymers with polymer thickness controlled at the nanoscale, with a subsequent analysis of glycans by mass spectrometry. A short section describing an active glycoprofiling by microengines (microrockets) is covered as well.This publication was made possible by NPRP grant number 6-381-1-078 from the Qatar National Research Fund (a member of the Qatar Foundation)Scopu