Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

A Deep Network Model for Paraphrase Detection in Short Text Messages

This paper is concerned with paraphrase detection. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Given two sentences, the objective is to detect whether they are semantically identical. An important insight from this work is that existing paraphrase systems perform well when applied on clean texts, but they do not necessarily deliver good performance against noisy texts. Challenges with paraphrase detection on user generated short texts, such as Twitter, include language irregularity and noise. To cope with these challenges, we propose a novel deep neural network-based approach that relies on coarse-grained sentence modeling using a convolutional neural network and a long short-term memory model, combined with a specific fine-grained word-level similarity matching model. Our experimental results show that the proposed approach outperforms existing state-of-the-art approaches on user-generated noisy social media data, such as Twitter texts, and achieves highly competitive performance on a cleaner corpus

A Progressive Clustering Algorithm to Group the XML Data by Structural and Semantic Similarity

Since the emergence in the popularity of XML for data representation and exchange over the Web, the distribution of XML documents has rapidly increased. It has become a challenge for researchers to turn these documents into a more useful information utility. In this paper, we introduce a novel clustering algorithm PCXSS that keeps the heterogeneous XML documents into various groups according to their similar structural and semantic representations. We develop a global criterion function CPSim that progressively measures the similarity between a XML document and existing clusters, ignoring the need to compute the similarity between two individual documents. The experimental analysis shows the method to be fast and accurate

An Extended Stable Marriage Problem Algorithm for Clone Detection

Code cloning negatively affects industrial software and threatens intellectual property. This paper presents a novel approach to detecting cloned software by using a bijective matching technique. The proposed approach focuses on increasing the range of similarity measures and thus enhancing the precision of the detection. This is achieved by extending a well-known stable-marriage problem (SMP) and demonstrating how matches between code fragments of different files can be expressed. A prototype of the proposed approach is provided using a proper scenario, which shows a noticeable improvement in several features of clone detection such as scalability and accuracy.Comment: 20 pages, 10 figures, 6 table

Reanalyzing language expectations: Native language knowledge modulates the sensitivity to intervening cues during anticipatory processing

Issue Online:21 September 2018We investigated how native language experience shapes anticipatory language processing. Two groups of bilinguals (either Spanish or Basque natives) performed a word matching task (WordMT) and a picture matching task (PictureMT). They indicated whether the stimuli they visually perceived matched with the noun they heard. Spanish noun endings were either diagnostic of the gender (transparent) or ambiguous (opaque). ERPs were time-locked to an intervening gender-marked determiner preceding the predicted noun. The determiner always gender agreed with the following noun but could also introduce a mismatching noun, so that it was not fully task diagnostic. Evoked brain activity time-locked to the determiner was considered as reflecting updating/reanalysis of the task-relevant preactivated representation. We focused on the timing of this effect by estimating the comparison between a gender-congruent and a gender-incongruent determiner. In the WordMT, both groups showed a late N400 effect. Crucially, only Basque natives displayed an earlier P200 effect for determiners preceding transparent nouns. In the PictureMT, both groups showed an early P200 effect for determiners preceding opaque nouns. The determiners of transparent nouns triggered a negative effect at similar to 430 ms in Spanish natives, but at similar to 550 ms in Basque natives. This pattern of results supports a "retracing hypothesis" according to which the neurocognitive system navigates through the intermediate (sublexical and lexical) linguistic representations available from previous processing to evaluate the need of an update in the linguistic expectation concerning a target lexical item.Spanish Ministry of Economy and Competitiveness (MINECO), Agencia Estatal de Investigación (AEI), Fondo Europeo de Desarrollo Regional (FEDER) (grant PSI2015‐65694‐P to N. M.), Spanish Ministry of Economy and Competitiveness “Severo Ochoa” Programme for Centres/Units of Excellence in R&D (grant SEV‐2015‐490

PDF-Malware Detection: A Survey and Taxonomy of Current Techniques

Portable Document Format, more commonly known as PDF, has become, in the last 20 years, a standard for document exchange and dissemination due its portable nature and widespread adoption. The flexibility and power of this format are not only leveraged by benign users, but from hackers as well who have been working to exploit various types of vulnerabilities, overcome security restrictions, and then transform the PDF format in one among the leading malicious code spread vectors. Analyzing the content of malicious PDF files to extract the main features that characterize the malware identity and behavior, is a fundamental task for modern threat intelligence platforms that need to learn how to automatically identify new attacks. This paper surveys existing state of the art about systems for the detection of malicious PDF files and organizes them in a taxonomy that separately considers the used approaches and the data analyzed to detect the presence of malicious code. © Springer International Publishing AG, part of Springer Nature 2018

Grounding semantics in robots for Visual Question Answering

In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning