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

In no uncertain terms : a dataset for monolingual and multilingual automatic term extraction from comparable corpora

Automatic term extraction is a productive field of research within natural language processing, but it still faces significant obstacles regarding datasets and evaluation, which require manual term annotation. This is an arduous task, made even more difficult by the lack of a clear distinction between terms and general language, which results in low inter-annotator agreement. There is a large need for well-documented, manually validated datasets, especially in the rising field of multilingual term extraction from comparable corpora, which presents a unique new set of challenges. In this paper, a new approach is presented for both monolingual and multilingual term annotation in comparable corpora. The detailed guidelines with different term labels, the domain- and language-independent methodology and the large volumes annotated in three different languages and four different domains make this a rich resource. The resulting datasets are not just suited for evaluation purposes but can also serve as a general source of information about terms and even as training data for supervised methods. Moreover, the gold standard for multilingual term extraction from comparable corpora contains information about term variants and translation equivalents, which allows an in-depth, nuanced evaluation

Towards Bilingual Term Extraction in Comparable Patents

PACLIC 23 / City University of Hong Kong / 3-5 December 200

TermEval 2020 : shared task on automatic term extraction using the Annotated Corpora for term Extraction Research (ACTER) dataset

The TermEval 2020 shared task provided a platform for researchers to work on automatic term extraction (ATE) with the same dataset: the Annotated Corpora for Term Extraction Research (ACTER). The dataset covers three languages (English, French, and Dutch) and four domains, of which the domain of heart failure was kept as a held-out test set on which final f1-scores were calculated. The aim was to provide a large, transparent, qualitatively annotated, and diverse dataset to the ATE research community, with the goal of promoting comparative research and thus identifying strengths and weaknesses of various state-of-the-art methodologies. The results show a lot of variation between different systems and illustrate how some methodologies reach higher precision or recall, how different systems extract different types of terms, how some are exceptionally good at finding rare terms, or are less impacted by term length. The current contribution offers an overview of the shared task with a comparative evaluation, which complements the individual papers by all participants

Exploring the use of parallel corpora in the complilation of specialised bilingual dictionaries of technical terms: a case study of English and isiXhosa

Text in EnglishAbstracts in English, isiXhosa and AfrikaansThe Constitution of the Republic of South Africa, Act 108 of 1996, mandates the state to take practical and positive measures to elevate the status and the use of indigenous languages. The implementation of this pronouncement resulted in a growing demand for specialised translations in fields like technology, science, commerce, law and finance. The lack of terminology and resources such as specialised bilingual dictionaries in indigenous languages, particularly isiXhosa remains a growing concern that hinders the translation and the intellectualisation of isiXhosa. A growing number of African scholars affirm the importance of specialised dictionaries in the African languages as tools for language and terminology development so that African languages can be used in the areas of science and technology. In the light of the background above, this study explored how parallel corpora can be interrogated using a bilingual concordancer, ParaConc to extract bilingual terminology that can be used to create specialised bilingual dictionaries. A corpus-based approach was selected due to its speed, efficiency and accuracy in extracting bilingual terms in their immediate contexts. In enhancing the research outcomes, Descriptive Translations Studies (DTS) and Corpus-based translation studies (CTS) were used in a complementary manner. Because the study is interdisciplinary, the function theories of lexicography that emphasise the function and needs of users were also applied. The analysis and extraction of bilingual terminology for dictionary making was successful through the use of the following ParaConc features, namely frequencies, hot word lists, hot words, search facility and concordances (Key Word in Context), among others. The findings revealed that English-isiXhosa Parallel Corpus is a repository of translation equivalents and other information categories that can make specialised dictionaries more user-friendly and multifunctional. The frequency lists were revealed as an effective method of selecting headwords for inclusion in a dictionary. The results also unraveled the complex functions of bilingual concordances where information on collocations and multiword units, sense distinction and usage examples could be easily identifiable proving that this approach is more efficient than the traditional method. The study contributes to the knowledge on corpus-based lexicography, standardisation of finance terminology resource development and making of user-friendly dictionaries that are tailor-made for different needs of users.Umgaqo-siseko weli loMzantsi Afrika ukhululele uRhulumente ukuba athabathe amanyathelo abonakalayo ekuphuhliseni nasekuphuculeni iilwimi zesiNtu. Esi sindululo sibangele ukwanda kokuguqulelwa kwamaxwebhu angezobuchwepheshe, inzululwazi, umthetho, ezemali noqoqosho angesiNgesi eguqulelwa kwiilwimi ebezifudula zingasiwe-so ezinjengesiXhosa. Ukunqongophala kwesigama kunye nezichazi-magama kube yingxaki enkulu ekuguquleleni ngakumbi izichazi-magama ezilwimi-mbini eziqulethe isigama esikhethekileyo. Iingcali ezininzi ziyangqinelana ukuba olu hlobo lwezi zichazi-magama luyimfuneko kuba ludlala iindima enkulu ekuphuhlisweni kweelwimi zesiNtu, ekuyileni isigama, nasekusetyenzisweni kwazo kumabakala obunzululwazi nobuchwepheshe. Olu phando ke luvavanya ukusetyenziswa kwekhophasi equlethe amaxwebhu esiNgesi neenguqulelo zawo zesiXhosa njengovimba wokudimbaza isigama sezemali esinokunceda ekuqulunqweni kwesichazi-magama esilwimi-mbini. Isizathu esibangele ukukhetha le ndlela yophando esebenzisa ikhompyutha kukuba iyakhawuleza, ulwazi oluthathwe kwikhophasi luchanekile, yaye isigama kwikhophasi singqamana ngqo nomxholo wamaxwebhu nto leyo eyenza kube lula ukufumana iintsingiselo nemizekelo ephilayo. Ukutyebisa olu phando indlela yekhophasi iye yaxhaswa zezinye iindlela zophando ezityunjiweyo: ufundo lwenguguqulelo oluchazayo (DTS) kunye neendlela zokuguqulela ezijoliswe kumsebenzi nakuhlobo lwabasebenzisi zinguqulelo ezo. Kanti ke ziqwalaselwe neenkqubo zophando lobhalo-zichazi-magama eziinjongo zokuqulunqa izichazi-magama ezesebenzisekayo neziluncedo kuninzi lwabasebenzisi zichazi-magama ngakumbi kwisizwe esisebenzisa iilwimi ezininzi. Ukuhlalutya nokudimbaza isigama kwikhophasi kolu phando kusetyenziswe isixhobo sekhompyutha esilungiselelwe ikhophasi enelwiimi ezimbini nangaphezulu ebizwa ngokuba yiParaConc. Iziphumo zolu phando zibonise mhlophe ukuba ikhophasi eneenguqulelo nguvimba weendidi ngendidi zamagama nolwazi olunokuphucula izichazi-magama zeli xesha. Kaloku abaguquleli basebenzise amaqhinga ngamaqhinga ukunika iinguqulelo bekhokelwa yimigomo nemithetho yoguqulelo enxuse abasebenzisi bamaxwebhu aguqulelweyo. Ubuchule beParaConc bokukwazi ukuhlela amagama ngokwendlela afumaneka ngayo kunye neenkcukacha zamanani budandalazise indlela eyiyo yokukhetha imichazwa enokungena kwisichazi-magama. Iziphumo zikwabonakalise iintlaninge yolwazi olufumaneka kwiKWIC, lwazi olo olungelula ukulufumana xa usebenzisa undlela-ndala wokwakha isichazi-magama. Esi sifundo esihlanganyele uGuqulelo olusekelwe kwiKhophasi noQulunqo-zichazi-magama zobuchwepheshe luya kuba negalelo elingathethekiyo kwindlela yokwakha izichazi-magama kwilwiimi zeSintu ngokubanzi nancakasana kwisiXhosa, nto leyo eya kothula umthwalo kubaqulunqi-zichazi-magama. Ukwakha nokuqulunqa izichazi-magama ezilwimi-mbini zezemali kuya kwandisa imithombo yesigama esinqongopheleyo kananjalo sivelise izichazi-magama eziluncedo kwisininzi sabantu.Die Grondwet van die Republiek van Suid-Afrika, Wet 108 van 1996, gee aan die staat die mandaat om praktiese en positiewe maatreëls te tref om die status en gebruik van inheemse tale te verhoog. Die implementering van hierdie uitspraak het gelei tot ’n toenemende vraag na gespesialiseerde vertalings in domeine soos tegnologie, wetenskap, handel, regte en finansies. Die gebrek aan terminologie en hulpbronne soos gespesialiseerde woordeboeke in inheemse tale, veral Xhosa, wek toenemende kommer wat die vertaling en die intellektualisering van Xhosa belemmer. ’n Toenemende aantal vakkundiges in Afrika beklemtoon die belangrikheid van gespesialiseerde woordeboeke in die Afrikatale as instrumente vir taal- en terminologie-ontwikkeling sodat Afrikatale gebruik kan word in die areas van wetenskap en tegnologie. In die lig van die voorafgaande agtergrond het hierdie studie ondersoek ingestel na hoe parallelle korpora deursoek kan word deur ’n tweetalige konkordanser (ParaConc) te gebruik om tweetalige terminologie te ontgin wat gebruik kan word in die onwikkeling van tweetalige gespesialiseerde woordeboeke. ’n Korpusgebaseerde benadering is gekies vir die spoed, doeltreffendheid en akkuraatheid waarmee dit tweetalige terme uit hulle onmiddellike kontekste kan onttrek. Beskrywende Vertaalstudies (DTS) en Korpusgebaseerde Vertaalstudies (CTS) is op ’n aanvullende wyse gebruik om die navorsingsuitkomste te verbeter. Aangesien die studie interdissiplinêr is, is die funksieteorieë van leksikografie wat die funksie en behoeftes van gebruikers beklemtoon, ook toegepas. Die analise en ontginning van tweetalige terminologie om woordeboeke te ontwikkel was suksesvol deur, onder andere, gebruik te maak van die volgende ParaConc-eienskappe, naamlik, frekwensies, hotword-lyste, hot words, die soekfunksie en konkordansies (Sleutelwoord-in-Konteks). Die bevindings toon dat ’n Engels-Xhosa Parallelle Korpus ’n bron van vertaalekwivalente en ander inligtingskategorieë is wat gespesialiseerde woordeboeke meer gebruikersvriendelik en multifunksioneel kan maak. Die frekwensielyste is geïdentifiseer as ’n doeltreffende metode om hoofwoorde te selekteer wat opgeneem kan word in ’n woordeboek. Die bevindings het ook die komplekse funksies van tweetalige konkordansers ontknoop waar inligting oor kollokasies en veelvuldigewoord-eenhede, betekenisonderskeiding en gebruiksvoorbeelde maklik identifiseer kon word wat aandui dat hierdie metode viii doeltreffender is as die tradisionele metode. Die studie dra by tot die kennisveld van korpusgebaseerde leksikografie, standaardisering van finansiële terminologie, hulpbronontwikkeling en die ontwikkeling van gebruikersvriendelike woordeboeke wat doelgemaak is vir verskillende behoeftes van gebruikers.Linguistics and Modern LanguagesD. Litt. et Phil. (Linguistics (Translation Studies)

META-NORD: Towards Sharing of Language Resources in Nordic and Baltic Countries

This paper introduces the META-NORD project which develops Nordic and Baltic part of the European open language resource infrastructure. META-NORD works on assembling, linking across languages, and making widely available the basic language resources used by developers, professionals and researchers to build specific products and applications. The goals of the project, overall approach and specific focus lines on wordnets, terminology resources and treebanks are described. Moreover, results achieved in first five months of the project, i.e. language whitepapers, metadata specification and IPR, are presented.Peer reviewe

many faces, many places (Term21)

UIDB/03213/2020 UIDP/03213/2020publishersversionpublishe

many faces, many places (Term21)

UIDB/03213/2020 UIDP/03213/2020Proceedings of the LREC 2022 Workshop Language Resources and Evaluation Conferencepublishersversionpublishe

D-TERMINE : data-driven term extraction methodologies investigated

Automatic term extraction is a task in the field of natural language processing that aims to automatically identify terminology in collections of specialised, domain-specific texts. Terminology is defined as domain-specific vocabulary and consists of both single-word terms (e.g., corpus in the field of linguistics, referring to a large collection of texts) and multi-word terms (e.g., automatic term extraction). Terminology is a crucial part of specialised communication since terms can concisely express very specific and essential information. Therefore, quickly and automatically identifying terms is useful in a wide range of contexts. Automatic term extraction can be used by language professionals to find which terms are used in a domain and how, based on a relevant corpus. It is also useful for other tasks in natural language processing, including machine translation. One of the main difficulties with term extraction, both manual and automatic, is the vague boundary between general language and terminology. When different people identify terms in the same text, it will invariably produce different results. Consequently, creating manually annotated datasets for term extraction is a costly, time- and effort- consuming task. This can hinder research on automatic term extraction, which requires gold standard data for evaluation, preferably even in multiple languages and domains, since terms are language- and domain-dependent. Moreover, supervised machine learning methodologies rely on annotated training data to automatically deduce the characteristics of terms, so this knowledge can be used to detect terms in other corpora as well. Consequently, the first part of this PhD project was dedicated to the construction and validation of a new dataset for automatic term extraction, called ACTER – Annotated Corpora for Term Extraction Research. Terms and Named Entities were manually identified with four different labels in twelve specialised corpora. The dataset contains corpora in three languages and four domains, leading to a total of more than 100k annotations, made over almost 600k tokens. It was made publicly available during a shared task we organised, in which five international teams competed to automatically extract terms from the same test data. This illustrated how ACTER can contribute towards advancing the state-of-the-art. It also revealed that there is still a lot of room for improvement, with moderate scores even for the best teams. Therefore, the second part of this dissertation was devoted to researching how supervised machine learning techniques might contribute. The traditional, hybrid approach to automatic term extraction relies on a combination of linguistic and statistical clues to detect terms. An initial list of unique candidate terms is extracted based on linguistic information (e.g., part-of-speech patterns) and this list is filtered based on statistical metrics that use frequencies to measure whether a candidate term might be relevant. The result is a ranked list of candidate terms. HAMLET – Hybrid, Adaptable Machine Learning Approach to Extract Terminology – was developed based on this traditional approach and applies machine learning to efficiently combine more information than could be used with a rule-based approach. This makes HAMLET less susceptible to typical issues like low recall on rare terms. While domain and language have a large impact on results, robust performance was reached even without domain- specific training data, and HAMLET compared favourably to a state-of-the-art rule-based system. Building on these findings, the third and final part of the project was dedicated to investigating methodologies that are even further removed from the traditional approach. Instead of starting from an initial list of unique candidate terms, potential terms were labelled immediately in the running text, in their original context. Two sequential labelling approaches were developed, evaluated and compared: a feature- based conditional random fields classifier, and a recurrent neural network with word embeddings. The latter outperformed the feature-based approach and was compared to HAMLET as well, obtaining comparable and even better results. In conclusion, this research resulted in an extensive, reusable dataset and three distinct new methodologies for automatic term extraction. The elaborate evaluations went beyond reporting scores and revealed the strengths and weaknesses of the different approaches. This identified challenges for future research, since some terms, especially ambiguous ones, remain problematic for all systems. However, overall, results were promising and the approaches were complementary, revealing great potential for new methodologies that combine multiple strategies