ymoslem/acl-abstracts huggingface dataset:
Exploring the Effectiveness of LLM Domain Adaptation for Business IT Machine Translation (2024)

In this paper, we study the translation abilities of Large Language Models (LLMs) for business IT texts.We are strongly interested in domain adaptation of translation systems, which is essential for accurate and lexically appropriate translation of such texts.Among the open-source models evaluated in a zero- and few-shot setting, we find Llama-2 13B the most promising for domain-specific translation fine-tuning.We investigate the full range of adaptation techniques for LLMs: from prompting, over parameter-efficient fine-tuning to full fine-tuning, and compare to classic neural machine translation (MT) models trained internally at SAP.We provide guidance how to use training budget most effectively for different fine-tuning approaches.We observe that while LLMs can translate on-par with SAP`s MT models on general domain data, it is difficult to close the gap on SAP`s domain-specific data, even with extensive training and carefully curated data.
Creating and Evaluating a Multilingual Corpus of UN General Assembly Debates (2024)

This paper presents a multilingual aligned corpus of political debates from the United Nations (UN) General Assembly sessions between 1978 and 2021, which covers five of the six official UN languages: Arabic, Chinese, English, French, Russian, and Spanish. We explain the preprocessing steps we applied to the corpus. We align the sentences by using word vectors to numerically represent the meaning of each sentence and then calculating the Euclidean distance between them. To validate our alignment methods, we conducted an evaluation study with crowd-sourced human annotators using Scale AI, an online platform for data labelling. The final dataset consists of around 300,000 aligned sentences for En-Es, En-Fr, En-Zh and En-Ru. It is publicly available for download.
Generating subject-matter expertise assessment questions with GPT-4: a medical translation use-case (2024)

This paper examines the suitability of a large language model (LLM), GPT-4, for generating multiple choice questions (MCQs) aimed at assessing subject matter expertise (SME) in the domain of medical translation. The main objective of these questions is to model the skills of potential subject matter experts in a human-in-the-loop machine translation (MT) flow, to ensure that tasks are matched to the individuals with the right skill profile. The investigation was conducted at Unbabel, an artificial intelligence-powered human translation platform. Two medical translation experts evaluated the GPT-4-generated questions and answers, one focusing on English{--}European Portuguese, and the other on English{--}German. We present a methodology for creating prompts to elicit high-quality GPT-4 outputs for this use case, as well as for designing evaluation scorecards for human review of such output. Our findings suggest that GPT-4 has the potential to generate suitable items for subject matter expertise tests, providing a more efficient approach compared to relying solely on humans. Furthermore, we propose recommendations for future research to build on our approach and refine the quality of the outputs generated by LLMs.
Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation (2024)

While large language models (LLMs) pre-trained on massive amounts of unpaired language data have reached the state-of-the-art in machine translation (MT) of general domain texts, post-editing (PE) is still required to correct errors and to enhance term translation quality in specialized domains. In this paper we present a pilot study of enhancing translation memories (TM) produced by PE (source segments, machine translations, and reference translations, henceforth called PE-TM) for the needs of correct and consistent term translation in technical domains. We investigate a light-weight two-step scenario where at inference time, a human translator marks errors in the first translation step, and in a second step a few similar examples are extracted from the PE-TM to prompt an LLM. Our experiment shows that the additional effort of augmenting translations with human error markings guides the LLM to focus on a correction of the marked errors, yielding consistent improvements over automatic PE (APE) and MT from scratch.
Estonian-Centric Machine Translation: Data, Models, and Challenges (2024)

Machine translation (MT) research is most typically English-centric. In recent years, massively multilingual translation systems have also been increasingly popular. However, efforts purposefully focused on less-resourced languages are less widespread. In this paper, we focus on MT from and into the Estonian language. First, emphasizing the importance of data availability, we generate and publicly release a back-translation corpus of over 2 billion sentence pairs. Second, using these novel data, we create MT models covering 18 translation directions, all either from or into Estonian. We re-use the encoder of the NLLB multilingual model and train modular decoders separately for each language, surpassing the original NLLB quality. Our resulting MT models largely outperform other open-source MT systems, including previous Estonian-focused efforts, and are released as part of this submission.
Transitude: Machine Translation on Social Media: MT as a potential tool for opinion (mis)formation (2024)

Misinformation on social media is a concern for content creators, consumers and regulators alike. Transitude looks at misinformation generated by machine translation (MT) through distortion of the intention and sentiment of text. It is the first study of MT`s impact on the formation of users' views of society through refugees in Ireland. It extends current MT evaluation methods with a new quality evaluation framework, producing the first dataset annotated for information distortion. It provides insights into the risks of relying on MT, with recommendations for users, developers, and policymakers.
Lightweight neural translation technologies for low-resource languages (2024)

The LiLowLa ({\textquotedblleft}Lightweight neural translation technologies for low-resource languages{\textquotedblright}) project aims to enhance machine translation (MT) and translation memory (TM) technologies, particularly for low-resource language pairs, where adequate linguistic resources are scarce. The project started in September 2022 and will run till August 2025.
MaTIAS: Machine Translation to Inform Asylum Seekers (2024)

This project aims to develop a multilingual notification system for asylum reception centres in Belgium using machine translation. The system will allow staff to communicate practical messages to residents in their own language. Ethnographically inspired fieldwork is being conducted in reception centres to understand current communication practices and ensure that the technology meets user needs. The quality and suitability of machine translation will be evaluated for three MT systems supporting all target languages. Automatic and manual evaluation methods will be used to assess translation quality, and terms of use, privacy and data protection conditions will be analysed.
SmartBiC: Smart Harvesting of Bilingual Corpora from the Internet (2024)

SmartBiC, an 18-month innovation project funded by the Spanish Government, aims at improving the full process of collecting, filtering and selecting in-domain parallel content to be used for machine translation and language model tuning purposes in industrial settings. Based on state-of-the-art technology in the free/open-source parallel web corpora harvester Bitextor, SmartBic develops a web-based application around it including novel components such as a language- and domain-focused crawler and a domain-specific corpora selector. SmartBic also addresses specific industrial use cases for individual components of the Bitextor pipeline, such as parallel data cleaning. Relevant improvements to the current Bitextor pipeline will be publicly released.
An Eye-Tracking Study on the Use of Machine Translation Post-Editing and Automatic Speech Recognition in Translations for the Medical Domain (2024)

This EAMT-funded eye-tracking study investigates the impact of Machine Translation Post-Editing and Automatic Speech Recognition on English-Romanian translations of patient-facing medical texts. This paper provides an overview of the study objectives, setup and preliminary results.
The MAKE-NMTViz Project: Meaningful, Accurate and Knowledge-limited Explanations of NMT Systems for Translators (2024)

This paper describes MAKE-NMTViz, a project designed to help translators visualize neural machine translation outputs using explainable artificial intelligence visualization tools initially developed for computer vision.
MULTILINGTOOL, Development of an Automatic Multilingual Subtitling and Dubbing System (2024)

In this paper, we present the MULTILINGTOOL project, led by the Elhuyar Foundation and funded by the European Commission under the CREA-MEDIA2022-INNOVBUSMOD call. The aim of the project is to develop an advanced platform for automatic multilingual subtitling and dubbing. It will provide support for Spanish, English, and French, as well as the co-official languages of Spain, namely Basque, Catalan, and Galician.
ERC Advanced Grant Project CALCULUS: Extending the Boundary of Machine Translation (2024)

The CALCULUS project, drawing on human capabilities of imagination and commonsense for natural language understanding (NLU), aims to advance machine-based NLU by integrating traditional AI concepts with contemporary machine learning techniques. It focuses on developing anticipatory event representations from both textual and visual data, connecting language structure to visual spatial organization and incorporating broad knowledge domains. Through testing these models in NLU tasks and evaluating their ability to predict untrained spatial and temporal details using real-world metrics, CALCULUS employs machine learning methods, including Bayesian techniques and neural networks, especially in data-sparse scenarios. The project`s culmination involves creating demonstrators that transform written stories into dynamic videos, showcasing the interdisciplinary expertise of the project leader in natural language processing, language and visual data analysis, information retrieval, and machine learning, all vital for the project`s achievements. In the CALCULUS project, our exploration of machine translation extends beyond the conventional text-to-text framework. We are broadening the horizons of machine translation by delving into the essence of transforming the formats of data distribution while keeping the meaning. This innovative approach involves converting information from one modality into another, transcending traditional linguistic boundaries. Our project includes novel work on translating text into images and videos, brain signals into images and videos.
GAMETRAPP project in progress: Designing a gamified environment for post-editing research abstracts (2024)

The {\guillemotleft}App for post-editing neural machine translation using gamification{\guillemotright} (GAMETRAPP) project (TED2021-129789B-I00), funded by the Spanish Ministry of Science and Innovation (2022{--}2024), has been in progress for a year. Thus, this paper presents its main goals and the analysis of neural machine translation and post-editing errors of research abstracts carried out. This leads to the designing of the gamified environment, which is currently under construction.
RCnum: A Semantic and Multilingual Online Edition of the Geneva Council Registers from 1545 to 1550 (2024)

The RCnum project is funded by the Swiss National Science Foundation and aims at producing a multilingual and semantically rich online edition of the Registers of Geneva Council from 1545 to 1550. Combining multilingual NLP, history and paleography, this collaborative project will clear hurdles inherent to texts manually written in 16th century Middle French while allowing for easy access and interactive consultation of these archives.
MTPE quality evaluation in translator education: the postedit.me app (2024)

This article presents the main functionality of the postedit.me app. Postedit.me is a software program that supports machine translation post-editing training in translator education, with special emphasis on standardized quality evaluation of post-edited texts produced by students. The app is made freely available to universities for teaching and research purposes.
Boosting Machine Translation with AI-powered terminology features (2024)

Artificial intelligence (AI) is quickly becoming an exciting new technology for the translation industry in form of large language models (LLMs). AI-based functionality could be used to improve the output of neural machine translation (NMT). One main issue that impacts MT quality and reliability is incorrect terminology. This is why STAR is making AI-powered terminology control a priority for its translation products because of the significant gains to be made - greatly improving the quality of MT output, reducing post editing (PE) costs and efforts, and thereby boosting overall translation productivity.
Automatic detection of (potential) factors in the source text leading to gender bias in machine translation (2024)

This research project aims to develop a comprehensive methodology to help make machine translation (MT) systems more gender-inclusive for society. The goal is the creation of a detection system, a machine learning (ML) model trained on manual annotations, that can automatically analyse source data and detect and highlight words and phrases that influence the gender bias inflection in target translations.The main research outputs will be (1) a manually annotated dataset, (2) a taxonomy, and (3) a fine-tuned model.
INCREC: Uncovering the creative process of translated content using machine translation (2024)

The INCREC project aims to uncover professional translators' creative stages to understand how technology can be best applied to the translation of literary and audio-visual texts, and to analyse the impact of these processes on readers and viewers. To better understand this process, INCREC triangulates data from eye-tracking, retrospective think-aloud inter-views, translated material, and questionnaires from professional translators and users.
SMUGRI-MT - Machine Translation System for Low-Resource Finno-Ugric Languages (2024)

We introduce SMUGRI-MT, an online neural machine translation system that covers 20 low-resource Finno-Ugric languages, along with seven high-resource languages.
plain X: 4-in-1 multilingual adaptation platform (2024)

plain X is a 4-in-1 solution for language adaptation. The software is an outcome of European HLT research and is by now in use as the major artificial-intelligence-powered human language pro-cessing platform at Deutsche Welle. plain X is a one-stop-shop for automated transcription, translation, subtitling and voice-over, with human correction options at all stages. We demonstrate how the platform works and show new features and developments of the platform in the framework of the SELMA project.
The BridgeAI Project (2024)

This paper describes the project {\textquotedblleft}BridgeAI: Boosting Regulatory Implementation with Data-driven insights, Global expertise, and Ethics for AI{\textquotedblright}, a one-year science-for-policy research project funded by the Portuguese Foundation for Science and Technology (FCT). The project aims to provide decision-makers in Portugal with the best context to implement the EU Artificial Intelligence (AI) Act and bridge the gap between AI research and policy. Although not exclusively on machine translation, the project pertains to natural language processing in general and ultimately to each of us as citizens.
GeFMT: Gender-Fair Language in German Machine Translation (2024)

Research on gender bias in Machine Translation (MT) predominantly focuses on binary gender or few languages. In this project, we investigate the ability of commercial MT systems and neural models to translate using gender-fair language (GFL) from English into German. We enrich a community-created GFL dictionary, and sample multi-sentence test instances from encyclopedic text and parliamentary speeches. We translate our resources with different MT systems and open-weights models. We also plan to post-edit biased outputs with professionals and share them publicly. The outcome will constitute a new resource for automatic evaluation and modeling gender-fair EN-DE MT.
ExU: AI Models for Examining Multilingual Disinformation Narratives and Understanding their Spread (2024)

Addressing online disinformation requires analysing narratives across languages to help fact-checkers and journalists sift through large amounts of data. The ExU project focuses on developing AI-based models for multilingual disinformation analysis, addressing the tasks of rumour stance classification and claim retrieval. We describe the ExU project proposal and summarise the results of a user requirements survey regarding the design of tools to support fact-checking.
Multilinguality in the VIGILANT project (2024)

VIGILANT (Vital IntelliGence to Investigate ILlegAl DisiNformaTion) is a three-year Horizon Europe project that will equip European Law Enforcement Agencies (LEAs) with advanced disinformation detection and analysis tools to investigate and prevent criminal activities linked to disinformation. These include disinformation instigating violence towards minorities, promoting false medical cures, and increasing tensions between groups causing civil unrest and violent acts. VIGILANT`s four LEAs require support for English, Spanish, Catalan, Greek, Estonian, Romanian and Russian. Therefore, multilinguality is a major challenge and we present the current status of our tools and our plans to improve their performance.
Evaluating Machine Translation for Emotion-loaded User Generated Content (TransEval4Emo-UGC) (2024)

This paper presents a dataset for evaluating the machine translation of emotion-loaded user generated content. It contains human-annotated quality evaluation data and post-edited reference translations. The dataset is available at our GitHub repository.
Community-driven machine translation for the Catalan language at Softcatal\`a (2024)

Among the services provided by Softcatal{\`a}, a non-profit 25-year-old grassroots organization that localizes software into Catalan and develops software to ease the generation of Catalan content, one of the most used is its machine translation (MT) service, which provides both rule-based MT and neural MT between Catalan and twelve other languages. Development occurs in a community-supported, transparent way by using free/open-source software and open language resources. This paper briefly describes the MT services at Softcatal{\`a}: the offered functionalities, the data, and the software used to provide them.
The MTxGames Project: Creative Video Games and Machine Translation -- Different Post-Editing Methods in the Translation Process (2024)

MTxGames is a doctoral research project examining three different machine translation (MT) post-editing (PE) methods in the context of translating creative texts from video games, focusing on translation speed, cognitive effort, quality, and translators' preferences. This is a mixed-methods study, eliciting quantitative data through keylogging, eye-tracking, and error evaluation as well as qualitative data through interviews. To create realistic experimental conditions, data elicitation takes place at the workplaces of freelancing professional game translators.
SignON -- a Co-creative Machine Translation for Sign and Spoken Languages (end-of-project results, contributions and lessons learned) (2024)

SignON, a 3-year Horizon 20202 project addressing the lack of technology and services for MT between sign languages (SLs) and spoken languages (SpLs) ended in December 2023. SignON was unprecedented. Not only it addressed the wider complexity of the aforementioned problem {--} from research and development of recognition, translation and synthesis, through development of easy-to-use mobile applications and a cloud-based framework to do the {\textquotedblleft}heavy lifting{\textquotedblright} as well as to establishing ethical, privacy and inclusivenesspolicies and operation guidelines {--} but also engaged with the deaf and hard of hearing communities in an effective co-creation approach where these main stakeholders drove the development in the right direction and had the final say.Currently we are witnessing advances in natural language processing for SLs, including MT. SignON was one of the largest projects that contributed to this surge with 17 partners and more than 60 consortium members, working in parallel with other international and European initiatives, such as project EASIER and others.
The Use of MT by humanitarian NGOs in Hong Kong (2024)

In the relief operations of international humanitarian organisations, non-governmental organisations (NGOs) often encounter language needs when delivering services (Tesseur 2022). This project examines the language needs of humanitarian NGOs working from Hong Kong and the solutions they adopted to overcome the language barriers when delivering international humanitarian relief to other countries.
HPLT`s First Release of Data and Models (2024)

The High Performance Language Technologies (HPLT) project is a 3-year EU-funded project that started in September 2022. It aims to deliver free, sustainable, and reusable datasets, models, and workflows at scale using high-performance computing. We describe the first results of the project. The data release includes monolingual data in 75 languages at 5.6T tokens and parallel data in 18 language pairs at 96M pairs, derived from 1.8 petabytes of web crawls. Building upon automated and transparent pipelines, the first machine translation (MT) models as well as large language models (LLMs) have been trained and released. Multiple data processing tools and pipelines have also been made public.
Literacy in Digital Environments and Resources (LT-LiDER) (2024)

LT-LiDER is an Erasmus+ cooperation project with two main aims. The first is to map the landscape of technological capabilities required to work as a language and/or translation expert in the digitalised and datafied language industry. The second is to generate training outputs that will help language and translation trainers improve their skills and adopt appropriate pedagogical approaches and strategies for integrating data-driven technology into their language or translation classrooms, with a focus on digital and AI literacy.
Cultural Transcreation with LLMs as a new product (2024)

We present how at Unbabel we have been using Large Language Models to apply a Cultural Transcreation (CT) product on customer support (CS) emails and how we have been testing the quality and potential of this product. We discuss our preliminary evaluation of the performance of different MT models in the task of translating rephrased content and the quality of the translation outputs. Furthermore, we introduce the live pilot programme and the corresponding relevant findings, showing that transcreated content is not only culturally adequate but it is also of high rephrasing and translation quality.