Web crawling and domain adaptation methods for building English–Greek machine translation systems for the culture/tourism domain

Informe técnico sobre el trabajo realizado por Víctor Manuel Sánchez Cartagena en una estancia en "Athena Research and Innovation Center", mientras estaba contratado por la empresa Prompsit Language Engineering y era colaborador honorífico en el Departamento de Lenguajes y Sistemas Informáticos de la Universidad de Alicante.This paper describes the process we followed in order to build English-Greek machine translation systems for the tourism/culture domain. We experimented with different data sets and domain adaptation methods for statistical machine translation and also built neural machine translation systems. The in-domain data were obtained by means of the ILSP Focused Crawler.The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement PIAP-GA-2012-324414 (Abu-MaTran)

Final FLaReNet deliverable: Language Resources for the Future - The Future of Language Resources

Language Technologies (LT), together with their backbone, Language Resources (LR), provide an essential support to the challenge of Multilingualism and ICT of the future. The main task of language technologies is to bridge language barriers and to help creating a new environment where information flows smoothly across frontiers and languages, no matter the country, and the language, of origin. To achieve this goal, all players involved need to act as a community able to join forces on a set of shared priorities. However, until now the field of Language Resources and Technology has long suffered from an excess of individuality and fragmentation, with a lack of coherence concerning the priorities for the field, the direction to move, not to mention a common timeframe. The context encountered by the FLaReNet project was thus represented by an active field needing a coherence that can only be given by sharing common priorities and endeavours. FLaReNet has contributed to the creation of this coherence by gathering a wide community of experts and making them participate in the definition of an exhaustive set of recommendations

Findings of the WMT'22 Shared Task on Large-Scale Machine Translation Evaluation for African Languages

We present the results of the WMT'22 Shared Task on Large-Scale Machine Translation Evaluation for African Languages. The shared task included both a data and a systems track, along with additional innovations, such as a focus on African languages and extensive human evaluation of submitted systems. We received 14 system submissions from 8 teams, as well as 6 data track contributions. We report a large progress in the quality of translation for African languages since the last iteration of this shared task: there is an increase of about 7.5 BLEU points across 72 language pairs, and the average BLEU scores went from 15.09 to 22.60

Survey of Low-Resource Machine Translation

International audienceWe present a survey covering the state of the art in low-resource machine translation (MT) research. There are currently around 7,000 languages spoken in the world and almost all language pairs lack significant resources for training machine translation models. There has been increasing interest in research addressing the challenge of producing useful translation models when very little translated training data is available. We present a summary of this topical research field and provide a description of the techniques evaluated by researchers in several recent shared tasks in low-resource MT

Wanca in Korp : Text corpora for underresourced Uralic languages

Peer reviewe

Border crossing and trespassing? : Expanding digital humanities research to developing peripheries with the novel digital technologies

Peer reviewe

UmobiTalk: Ubiquitous Mobile Speech Based Learning Language Translator for Sesotho Language

Published ThesisThe need to conserve the under-resourced languages is becoming more urgent as some of them are becoming extinct; natural language processing can be used to redress this. Currently, most initiatives around language processing technologies are focusing on western languages such as English and French, yet resources for such languages are already available. The Sesotho language is one of the under-resourced Bantu languages; it is mostly spoken in Free State province of South Africa and in Lesotho. Like other parts of South Africa, Free State has experienced high number of migrants and non-Sesotho speakers from neighboring provinces and countries; such people are faced with serious language barrier problems especially in the informal settlements where everyone tends to speak only Sesotho. Non-Sesotho speakers refers to the racial groups such as Xhosas, Zulus, Coloureds, Whites and more, in which Sesotho language is not their native language. As a solution to this, we developed a parallel corpus that has English as source and Sesotho as a target language and packaged it in UmobiTalk - Ubiquitous mobile speech based learning translator. UmobiTalk is a mobile-based tool for learning Sesotho for English speakers. The development of this tool was based on the combination of automatic speech recognition, machine translation and speech synthesis

An investigation of English-Irish machine translation and associated resources

As an official language in both Ireland and the European Union (EU), there is a high demand for English-Irish (EN-GA) translation in public administration. The difficulty that translators currently face in meeting this demand leads to the need for reliable domain-specific user-driven EN-GA machine translation (MT). This landscape provides a timely opportunity to address some research questions surrounding MT for the EN-GA language pair. To this end, we assess the corpora available for training data-driven MT systems, including publicly-available data, data collected through EU-supported data collection efforts and web-crawling, showing that though Irish is a low-resource language it is possible to increase the corpora available through concerted data collection efforts. We investigate how increased corpora affect domain-specific (public administration) statistical MT (SMT) and neural MT (NMT) systems using automatic metrics. The effect that different SMT and NMT parameters have on these automatic values is also explored, using sentence-level metrics to identify specific areas where output differs greatly between MT systems and providing a linguistic analysis of each. With EN-GA SMT and NMT automatic evaluation scores showing inconclusive results, we investigate the usefulness of EN-GA hybrid MT through the use of monolingual data as a source of artificial data creation via backtranslation. We evaluate these results using automatic metrics and linguistic analysis. Although results indicate that the addition of artificial data did not have a positive impact on EN-GA MT, repeated experiments involving Scottish Gaelic show that the method holds promise, given suitable conditions. Finally, given that the intended use-case of EN-GA MT is in the workflow of a professional translator, we conduct an in-depth human evaluation study for EN-GA SMT and NMT, providing a human-derived assessment of EN-GA MT quality and comparison of EN-GA SMT and NMT. We include a survey of translator opinions and recommendations surrounding EN-GA SMT and NMT as well as an analysis of data gathered through the post-editing of MT output. We compare these results to those generated automatically and provide recommendations for future work on EN-GA MT, in particular with regards to its use in a professional translation workflow within public administration

Report on the Finnish Language

Language-centric AI is already ubiquitous and language technology is in its intrinsic core. As was stated in the report The Finnish Language in the Digital Age (Koskenniemi et al., 2012): “If there is adequate language technology available, it will be able to ensure the survival of languages with small populations of speakers.” During the last ten years, digitalisation has changed the way we communicate and interact in the world creating an increasing demand for language-based AI services. New skills are needed to be able to cope in the digital world, so digital education and media awareness are now taught in elementary schools. Digital skills are considered new citizen skills. To provide language-based services to an increasing number of users, we need applications that are built on AI, as well as to provide routine services to special groups and to meet accessibility requirements. The still small number of existing applications and services is partly due to the lack of language resources. Also, the small size of the Finnish market area has affected this when large corporations have primarily focused on English with only some support for Finnish in high-demand products in the Finnish market. In the field of language technology, the Finnish language is still only moderately equipped with products, technologies and resources. There are applications and tools for speech synthesis, speech recognition, information retrieval, spelling correction and grammar checking. There are also a few applications for automatically translating language. The situation has improved during the last 10 years, but still support for automated translation leaves room for ample improvement and the general support for spoken language is modest in industry applications although some recent research results are encouraging