ymoslem/acl-abstracts huggingface dataset:
Use of NMT in Ubiqus Group (2018)

After more than 30 years' experience as a translator and as a reviser, I have recently started to post-edit. During these 10 months discovering a new approach to my profession, the experience has been highly positive. Ubiqus, the French group to which we belong, has developed 20 engines based on OpenNMT. OpenNMT derives from an academic project initiated in 2016 by Harvard NLP; Systran joined the project and an open source toolkit was released in January 2017. The community grew when individuals as well as localization professionals contributed. Ubiqus adopted this toolkit at the very beginning of 2017 and contributed to its development as well as with some extensions, developing a layer to integrate OpenNMT in our workflow environments, including SDL Studio and with our internal ERP, which enables to provide a highly efficient end-to-end system. I have been using the EN-ES and FR-ES engines mainly for legal texts. I very soon felt comfortable with the task, I started measuring my productivity by timing my output. I was surprised by the improvement since the very beginning, and as the NMT engine was further trained and I got more used to the post-editing task I achieved even better results, improving productivity by almost 30{\%}. Ubiqus has also developed a scheme for the systematic scoring of all translation jobs, U-Score, a composite indicator of the overall performance of the machine. The U-Score is obtained by aggregating the information of BLEU, TER and DL-ratio and averaging them. It then performs a transformation allowing to spread the scale a bit. The scores have been clearly improving in the last months with a constant training of the engines.
OctaveMT: Putting Three Birds into One Cage (2018)

This product presentation describes the integration of the three MT technologies currently used {--} rule-based (RBMT), Statistical (SMT) and Neural (NMT) {--} into one scalable single platform, OctaveMT. MT clients can access all three types of MT engines, whether on a user specified basis or depending on several translation parameters (language-direction, domain, etc.)
TransPerfect`s Private Neural Neural Machine Translation Portal (2018)

We will present our solution to replace the usage of publicly available machine translation (MT) services in companies where privacy and confidentiality are key. Our MT portal can translate across a variety of languages using neural machine translation, and supports an extensive number of file types. Corporations are using it to enable multilingual communication everywhere.
Terminology validation for MT output (2018)

WebTerm Connector is a plugin for STAR MT Translate which combines machine translation with validated terminology information. The aim is to provide {\textquotedblleft}understandable{\textquotedblright} information in the target language using corporate language and terminology.
The ModernMT Project (2018)

This short presentation introduces ModernMT: an open-source project 1 that integrates real-time adaptive neural machine translation into a single easy-to-use product.
Developing a New Swiss Research Centre for Barrier-Free Communication (2018)

The project {\textquoteleft}Proposal and Implementation of a Swiss Research Centre for Barrier-free Communication' (BFC) is a four-year project (2017{--}2020) funded by the Rectors' Conference of Swiss Higher Education Institutions (swissuniversities).1 Its purpose is to ensure that individuals with a visual or hearing disability, people with a temporary cognitive impairment and speakers without sufficient knowledge of local languages can communicate and enjoy barrier-free access to information in all spheres of life, with a special focus on higher education.
Massively multilingual accessible audioguides via cell phones (2018)

Bidaide1 is a web service that allows the visitors of a museum, route or building to read or listen to explanations relative to the visited place on their own mobile and in their own language. The visitor can access the explanations in various ways: by scanning some QR codes located in the place, by GPS positioning (in outdoor routes), or by automatic Bluetooth proximity activation. This makes it accessible for people with reduced or null vision. On the other hand, this platform also offers to the manager of the visited site the most advanced language resources to create the texts and audios of the explanations in many languages.
ELRI - European Language Resources Infrastructure (2018)

We describe the European Language Resources Infrastructure project, whose main aim is the provision of an infrastructure to help collect, prepare and share language resources that can in turn improve translation services in Europe.
The SUMMA Platform: Scalable Understanding of Multilingual Media (2018)

We present the latest version of the SUMMA platform, an open-source software platform for monitoring and interpreting multi-lingual media, from written news published on the internet to live media broadcasts via satellite or internet streaming.
Smart Pre- and Post-Processing for STAR MT Translate (2018)

After many successful experiments it has become evident that smart pre- and post-processing can significantly improve the output of neural machine translation. Therefore, various generic and language-specific processes are applied to the training corpus, the user input and the MT output for STAR MT Translate.
mtrain: A Convenience Tool for Machine Translation (2018)

We present mtrain, a convenience tool for machine translation. It wraps existing machine translation libraries and scripts to ease their use. mtrain is written purely in Python 3, well-documented, and freely available.1
Empowering Translators with MTradum\`atica: A Do-It-Yourself statistical machine translation platform (2018)

According to Torres Hostench et al. (2016), the use of machine translation (MT) in Catalan and Spanish translation companies is low. Based on these results, the Tradum{\`a}tica research group,2 through the ProjecTA and ProjecTA-U projects,3 set to bring MT and translators closer with a two-fold strategy. On the one hand, by developing MTradum{\`a}tica, a free Moses-based web platform with graphical user interface (GUI) for statistical machine translation (SMT) trainers. On the other hand, by including MT-related contents in translators' training. This paper will describe the latest developments in MTradum{\`a}tica.
Speech Translation Systems as a Solution for a Wireless Earpiece (2018)

The advances of deep learning approaches in automatic speech recognition (ASR) and machine translation (MT) have allowed for levels of accuracy that move speech translation closer to being a commercially viable alternative interpretation solution. In addition, recent improvements in micro-electronic mechanical systems, microphone arrays, speech processing software, and wireless technology have enabled speech recognition software to capture higher quality speech input from wireless earpiece products. With this in mind, we introduce and present a wearable speech translation tool called Pilot, which uses these systems to translate language spoken within the proximity of a user wearing the wireless earpiece.
Multi-modal Context Modelling for Machine Translation (2018)

MultiMT is an European Research Council Starting Grant whose aim is to devise data, methods and algorithms to exploit multi-modal information (images, audio, metadata) for context modelling in machine translation and other cross- lingual tasks. The project draws upon different research fields including natural language processing, computer vision, speech processing and machine learning.
Project PiPeNovel: Pilot on Post-editing Novels (2018)

Given (i) the rise of a new paradigm to machine translation based on neural networks that results in more fluent and less literal output than previous models and (ii) the maturity of machine-assisted translation via post-editing in industry, project PiPeNovel studies the feasibility of the post-editing workflow for literary text conducting experiments with professional literary translators.
Smart Computer-Aided Translation Environment (SCATE): Highlights (2018)

We present the highlights of the now finished 4-year SCATE project. It was completed in February 2018 and funded by the Flemish Government IWT-SBO, project No. 130041.1
news.bridge - Automated Transcription and Translation for News (2018)

news.bridge provides a platform for multilingual video processing, including automated transcription and translation, subtitling, voice-over, and summarization, with post-editing facility of videos in a broad range of languages. The platform is currently in beta testing at Deutsche Welle for republishing of videos in other languages.
Europarl Datasets with Demographic Speaker Information (2018)

Research on speaker-adapted neural machine translation (NMT) is scarce. One of the main challenges for more personalized MT systems is finding large enough annotated parallel datasets with speaker information. Rabinovich et al. (2017) published an annotated parallel dataset for EN{--}FR and EN{--}DE, however, for many other language pairs no sufficiently large annotated datasets are available.
With a little help from NLP: My Language Technology applications with impact on society (2018)

The keynote speech presents the speaker`s vision that research should lead to the development of applications which benefit society. To support this, the speaker will present three original methodologies proposed by him which underpin applications jointly implemented with colleagues from across his research group. These Language Technology tools already have a substantial societal impact in the following areas: learning and assessment, translation and care for people with language disabilities.
NLP-based Assessment of Reading Efficiency in Early Grade Children (2018)

Assessing reading skills is a laborious and time-consuming task, which requires monitoring a variety of interlocked abilities, ranging from accurate word rendering, reading fluency and lexical access, to linguistic comprehension, and interpretation, management and inference of complex events in working memory. No existing software, to our knowledge, is able to cover and integrate reading performance monitoring, instant feedback, personalised potentiation and intelligent decision support to teachers and speech therapists, assessment of response to intervention. NLP and ICT technologies can make such an ambitious platform an achievable target.
Figurative language processing: A developmental and NLP Perspective (2018)

It is now common to employ evidence from human behaviour (e.g., child development) for the creation of computational models of this behaviour with a variety of applications (e.g., in developmental robotics). In this paper we address research in the comprehension and processing of figurative (non-literal) language in highly verbal individuals with autism in comparison with age- and language level-matched neuro-typical individuals and discuss critically what factors might account for the observed problems. Based on this evidence we try to outline the strategies used by human language users in understanding non-literal/non-compositional expressions and proceed to identifying possible solutions for automated language systems in the domain of idiomatic expressions.
Abstractive Text Summarization with Application to Bulgarian News Articles (2018)

With the development of the Internet, a huge amount of information is available every day. Therefore, text summarization has become critical part of our first access to the information. There are two major approaches for automatic text summarization: abstractive and extractive. In this work, we apply abstractive summarization algorithms on a corpus of Bulgarian news articles. In particular, we compare selected algorithms of both techniques and we show results which provide evidence that the selected state-of-the-art algorithms for abstractive text summarization perform better than the extractive ones for articles in Bulgarian. For the purpose of our experiments we collected a new dataset consisting of around 70,000 news articles and their topics. For research purposes we are also sharing the tools to easily collect and process such datasets.
Towards Lexical Meaning Formal Representation by virtue of the NL-DL Definition Transformation Method (2018)

The paper represents a part of an extensive study devoted to the issues of lexical meaning formal representation in OWL 2 DL notation. Both theoretical and methodological aspects of lexical meaning formalization within the framework of an ontology are observed in the paper. Model-theoretic semantics paradigm and Kripke model are considered to form a theoretical background for formalization of lexical meaning, whereas the NL-DL definition transformation method is investigated as a method designed to provide us with acceptable formal definitions in OWL 2 DL notation with natural language definitions given at the input. A brief critical study of the method has allowed to reveal particular problematic cases of the method application, which arise due to syntactic peculiarities of natural language definitions given at the input.
Narrow Productivity, Competition, and Blocking in Word Formation (2018)

The present study explores the productivity of word formation processes in English, focusing on word composition by suffixes such as ​-ize (e.g. transcendentalize​), ​-(a)(t)ion (​territorization)​ , and ​-al (​realizational​). An optimal productivity measure for affixation is identified, which makes best use of hapax legomena in a large-scale corpus and attaches great importance to the base forms of an affix. This measure is then applied to the data collected from a large corpus to compute the productivity values of twelve kinds of affixes. The detailed investigation reveals that (i) the high productivity rate of an affix demonstrates a creative aspect of the affix, giving full support to the idea of {\textquotedblleft}generative{\textquotedblright} morphology, (ii) productivity is gradient; very high, fairly high, and low productivity of affixes are recognizable, and (iii) this is necessarily reflected in determining the word form of a derivative (cf. ​territoriz​ation​); competition is carried out to decide which affix is selected for a given base form (​territorize​) and the {\textquotedblleft}losers{\textquotedblright} (​-ment/-al​) are blocked out.
Knowledge and Rule-Based Diacritic Restoration in Serbian (2018)

In this paper we present a procedure for the restoration of diacritics in Serbian texts written using the degraded Latin alphabet. The procedure relies on the comprehensive lexical resources for Serbian: the morphological electronic dictionaries, the Corpus of Contemporary Serbian and local grammars. Dictionaries are used to identify possible candidates for the restoration, while the data obtained from SrpKor and local grammars assists in making a decision between several candidates in cases of ambiguity. The evaluation results reveal that, depending on the text, accuracy ranges from 95.03{\%} to 99.36{\%}, while the precision (average 98.93{\%}) is always higher than the recall (average 94.94{\%}).
Perfect Bulgarian Hyphenation and or How not to Stutter at End-of-line (2018)

What is Perfect Bulgarian Hyphenation? We know that it has to be based somehow on the syllables and on the morphology but considering that these two factors often contradict each other, how exactly are we going to combine them? And speaking about syllables, what are they and how are we going to determine them? Also, how are we going to find the morphemes in the words? Don`t we have to develop an electronic derivational dictionary of the Bulgarian language? Isn`t all this going to be forbiddingly difficult?
Russian Bridging Anaphora Corpus (2018)

In this paper, we present a bridging anaphora corpus for Russian, introduce a syntactic approach for bridging annotation and discuss the difference between the syntactic and semantic approaches. We also discuss some special aspects of bridging annotation for Russian and other languages where definite nominal groups are not marked so frequently as e.g. in Romance or Germanic languages. In the end we list the main cases of annotator disagreement.
Aspectual and Temporal Characteristics of the Past Active Participles in Bulgarian -- a Corpus-based Study (2018)

The paper presents a corpus-based study of the past active participles in Bulgarian with respect of their aspectual and temporal characteristics. As this type of participles combine two morphological markers, a special attention is paid on their interaction in different tenses, moods and evidentials. The source of language material used for the study is the Bulgarian National Corpus. The paper is organized in terms of morphological oppositions, aspectual and temporal, analyzing the functions of the participles in compound verbal forms.
Unmatched Feminitives in a Corpus of Bulgarian and Ukrainian Parallel Texts (2018)

Feminitives are formed and used in all Slavic languages, but the productivity of their formation and the intensity of their use are not the same everywhere. They are often subject to various intralinguistic and extralinguistic restrictions. In this paper we present a study of feminitives based on a parallel Bulgarian{--} Ukrainian corpus, with a focus on those occasions on which a feminitive in one language corresponds to a masculine (rarely neuter) noun in the other. The experiment shows that Bulgarian uses feminitives with considerably greater regularity than Ukrainian does, and we discuss the semantic classes of nouns that fail to form feminitives most often and the efect of the source language in translated text and of the author`s and translator`s individual preferences.
The Bulgarian Summaries Corpus (2018)

This article aims to present the Bulgarian Summaries Corpus, its advantages, its purpose and why it is necessary. It explains the selection of texts and process of summarization and the tool used, in addition of a quick overview of the current situation in Bulgaria. The paper also presents a general outline of the market needs, the use of this kind of tools and a short list of examples of a variety of corpora around the world both in language and field.
Ontologies for Natural Language Processing: Case of Russian (2018)

The paper describes the RuThes family of Russian thesauri intended for natural language processing and information retrieval applications. RuThes-like thesauri include, besides RuThes, Sociopolitical thesaurus, Security Thesaurus, and Ontology on Natural Sciences and Technologies. The RuThes format is based on three approaches for developing computer resources: Princeton WordNet, information-retrieval thesauri, and formal ontologies. The published version of RuThes thesaurus (RuThes-lite 2.0) became a basis for semi-automatic generation of RuWordNet, WordNet-like thesaurus for Russian. Currently researchers can use both RuThes-lite or RuWordNet and compare them in applications. Other RuThes-like resources are being prepared to publication.
Resource-based WordNet Augmentation and Enrichment (2018)

In this paper we present an approach to support production of synsets for Serbian WordNet (SerWN) by adjusting Princeton WordNet (PWN) synsets using several bilingual English-Serbian resources. PWN synset definitions were automatically translated and post-edited, if needed, while candidate literals for Serbian synsets were obtained automatically from a list of translational equivalents compiled form bilingual resources. Preliminary results obtained from a set of 1248 selected PWN synsets show that the produced Serbian synsets contain 4024 literals, out of which 2278 were offered by the system we present in this paper, whereas experts added the remaining 1746. Approximately one half of synset definitions obtained automatically were accepted with no or minor corrections. These first results are encouraging, since the efficiency of synset production for SerWN was increased. There is also space for further improvement of this approach to wordnet enrichment.
Classifying Verbs in WordNet by Harnessing Semantic Resources (2018)

This paper presents the principles and procedures involved in the construction of a classification of verbs using information from 3 semantic resources {--} WordNet, FrameNet and VerbNet. We adopt the FrameNet frames as the primary categories of the proposed classification and transfer them to WordNet synsets. The hierarchical relationships between the categories are projected both from the hypernymy relation in WordNet and from the hierarchy of some of the frame-to-frame relations in FrameNet. The semantic classes and their hierarchical organisation in WordNet are thus made explicit and allow for linguistic generalisations on the inheritance of semantic features and structures. We then select the beginners of the separate hierarchies and assign classification categories recursively to their hyponyms using a battery of procedures based on generalisations over the semantic primes and the hierarchical structure of WordNet and FrameNet and correspondences between VerbNet superclasses and FrameNet frames. The so-obtained suggestions are ranked according to probability. As a result, 13,465 out of 14,206 verb synsets are accommodated in the classification hierarchy at least through a general category, which provides a point of departure towards further refinement of categories. The resulting system of classification categories is initially derived from the WordNet hierarchy and is further validated against the hierarchy of frames within FrameNet. A set of procedures is established to address inconsistencies and heterogeneity of categories. The classification is subject to ongoing extensive manual verification, essential for ensuring the quality of the resource.
A Pilot Study for Enriching the Romanian WordNet with Medical Terms (2018)

This paper presents the preliminary investigations in the process of integrating a specialized vocabulary, namely medical terminology, into the Romanian wordnet. We focus here on four classes from this vocabulary: anatomy (or body parts), disorders, medical procedures and chemicals. In this pilot study we selected two large concepts from each class and created the Romanian terminological (sub)trees for each of them, starting from a medical thesaurus (SNOMED CT) and translating the terms, process which raised various challenges, all of them asking for the expertise of a specialist in the health care domain. The integration of these (sub)trees in the Romanian wordnet also required careful decision making, given the structural differences between a wordnet and a terminological thesaurus. They are presented and discussed herein.
Factors and Features Determining the Inheritance of Semantic Primes between Verbs and Nouns within WordNet (2018)

The paper outlines the mechanisms of inheriting semantic content between verbs and nouns as a result of derivational relations. The main factors determining the inheritance are: (1) the semantic class of the verb as represented by the noun; (2) the subcategorisation frame and argument structure of the verb predicate; (3) the derivational relation between the verb and the noun, as well as the resulting semantic relation made explicit through the derivation; (4) hierarchical relations within WordNet. The paper explores three types of verb-noun prime inheritance relations: (a) universal {--} not depending on the argument structure, which are eventive or circumstantial; (b) general {--} specific to classes of verbs, for example agentive or non-agentive; (c) verb-specific {--} depending on the specific subcategorisation frame of the verb as presented in VerbNet and/or FrameNet. The paper presents a possibility for extended coverage of semantic relations based on information about the argument structure of verbs. Further, the work focuses on the regularities in the way in which derivationally related nouns inherit semantic characteristics of the predicate. These regularities can be applied for the purposed of predicting derivationally and semantically related synsets within WordNet, as well as for the creation of language specific synsets, for consistency checks and verification.