• Digital Humanities and Cultural Heritage
• 2013-2022close
• Publications close
• Research software close
• Contribution for newspaper or weekl... close
• European Commission close
• EU close

Automated Essay Scoring has gained a wider applicability and usage with the integration of advanced Natural Language Processing techniques which enabled in-depth analyses of discourse in order capture the specificities of written texts. In this paper, we introduce a novel Automatic Essay Scoring method for Dutch language, built within the Readerbench framework, which encompasses a wide range of textual complexity indices, as well as an automated segmentation approach. Our method was evaluated on a corpus of 173 technical reports automatically split into sections and subsections, thus forming a hierarchical structure on which textual complexity indices were subsequently applied. The stepwise regression model explained 30.5% of the variance in students’ scores, while a Discriminant Function Analysis predicted with substantial accuracy (75.1%) whether they are high or low performance students.

Automatic translation of documents is an important task in many domains, in- cluding the biological and clinical do- mains. The second edition of the Biomed- ical Translation task in the Conference of Machine Translation focused on the au- tomatic translation of biomedical-related documents between English and various European languages. This year, we ad- dressed ten languages: Czech, German, English, French, Hungarian, Polish, Por- tuguese, Spanish, Romanian and Swedish. Test sets included both scientific publica- tions (from the Scielo and EDP Sciences databases) and health-related news (from the Cochrane and UK National Health Ser- vice web sites). Seven teams participated in the task, submitting a total of 82 runs. Herein we describe the test sets, participat- ing systems and results of both the auto- matic and manual evaluation of the trans- lations.

Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. In this work, we are the first to incorporate information about predicate-argument structure of source sentences (namely, semantic-role representations) into neural machine translation. We use Graph Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders and achieve improvements in BLEU scores over the linguistic-agnostic and syntax-aware versions on the English--German language pair.

This paper introduces and summarises the findings of a new shared task at the intersection of Natural Language Processing and Computer Vision: the generation of image descriptions in a target language, given an image and/or one or more descriptions in a different (source) language. This challenge was organised along with the Conference on Machine Translation (WMT16), and called for system submissions for two task variants: (i) a translation task, in which a source language image description needs to be translated to a target language, (optionally) with additional cues from the corresponding image, and (ii) a description generation task, in which a target language description needs to be generated for an image, (optionally) with additional cues from source language descriptions of the same image. In this first edition of the shared task, 16 systems were submitted for the translation task and seven for the image description task, from a total of 10 teams.

Abstract The paper studies the energy renovation of a residential building with new façade solutions combining smart ventilated window (VW) and PCM energy storage and the corresponding control strategy to ensure energy savings. The study is carried out by Energyplus modelling comparing the energy consumption and thermal comfort of an apartment before and after renovation. A detailed control strategy is introduced and simulated. The modelling results of the apartment before and after retrofit indicate that with the designed control strategies, the average energy saving percentage of the apartment with PCM energy storage and VW compared to the apartment without PCM energy storage and VW is 29%. The rooms with PCMVWs achieve higher energy saving than the rooms with only VWs. The PCM energy storage improves energy performance of the VWs for both heating and cooling seasons. With the renovation, the thermal comfort of all the rooms are improved for cooling season.

In this paper, we investigate different approaches for dialect identification in Arabic broadcast speech. These methods are based on phonetic and lexical features obtained from a speech recognition system, and bottleneck features using the i-vector framework. We studied both generative and discriminative classifiers, and we combined these features using a multi-class Support Vector Machine (SVM). We validated our results on an Arabic/English language identification task, with an accuracy of 100%. We also evaluated these features in a binary classifier to discriminate between Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%. We further reported results using the proposed methods to discriminate between the five most widely used dialects of Arabic: namely Egyptian, Gulf, Levantine, North African, and MSA, with an accuracy of 59.2%. We discuss dialect identification errors in the context of dialect code-switching between Dialectal Arabic and MSA, and compare the error pattern between manually labeled data, and the output from our classifier. All the data used on our experiments have been released to the public as a language identification corpus.

A key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. However, this ambition is largely hampered by the variation in structural and semantic properties, i.e. the typological profiles of the world's languages. In this work, we analyse the implications of this variation on the language modeling (LM) task. We present a large-scale study of state-of-the art n-gram based and neural language models on 50 typologically diverse languages covering a wide variety of morphological systems. Operating in the full vocabulary LM setup focused on word-level prediction, we demonstrate that a coarse typology of morphological systems is predictive of absolute LM performance. Moreover, fine-grained typological features such as exponence, flexivity, fusion, and inflectional synthesis are borne out to be responsible for the proliferation of low-frequency phenomena which are organically difficult to model by statistical architectures, or for the meaning ambiguity of character n-grams. Our study strongly suggests that these features have to be taken into consideration during the construction of next-level language-agnostic LM architectures, capable of handling morphologically complex languages such as Tamil or Korean.