pwc-archive/papers-with-abstracts huggingface dataset:
Comparing Multilingual NMT Models and Pivoting
Authors: Celia Soler Uguet, Fred Bane, Anna Zaretskaya, Tània Blanch Miró
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, NMT, Translation

Following recent advancements in multilingual machine translation at scale, our team carried out tests to compare the performance of multilingual models (M2M from Facebook and multilingual models from Helsinki-NLP) with a two-step translation process using English as a pivot language. Direct assessment by linguists rated translations produced by pivoting as consistently better than those obtained from multilingual models of similar size, while automated evaluation with COMET suggested relative performance was strongly impacted by domain and language family.
Pre-training Synthetic Cross-lingual Decoder for Multilingual Samples Adaptation in E-Commerce Neural Machine Translation
Authors: Kamal Kumar Gupta, Soumya Chennabasavraj, Nikesh Garera, Asif Ekbal
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Decoder, Incremental Learning, Machine Translation, NMT, Translation

Availability of the user reviews in vernacular languages is helpful for the users to get information regarding the products. Since most of the e-commerce websites allow the reviews in English language only, it is important to provide the translated versions of the reviews to the non-English speaking users. Translation of the user reviews from English to vernacular languages is a challenging task, predominantly due to the lack of sufficient in-domain datasets. In this paper, we present a pre-training based efficient technique which is used to adapt and improve the single multilingual neural machine translation (NMT) model for the low-resource language pairs. The pre-trained model contains a special synthetic cross-lingual decoder. The decoder for the pre-training is trained over the cross-lingual target samples where the phrases are replaced with their translated counterparts. After pre-training, the model is adapted to multiple samples of the low-resource language pairs using incremental learning that does not require full training from the very scratch. We perform the experiments over eight low-resource and three high resource language pairs from the generic domain, and two language pairs from the product review domains. Through our synthetic multilingual decoder based pre-training, we achieve improvements of upto 4.35 BLEU points compared to the baseline and 2.13 BLEU points compared to the previous code-switched pre-trained models. The review domain outputs from the proposed model are evaluated in real time by human evaluators in the e-commerce company Flipkart.
Error Annotation in Post-Editing Machine Translation: Investigating the Impact of Text-to-Speech Technology
Authors: Justus Brockmann, Claudia Wiesinger, Dragoș Ciobanu
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, text-to-speech, Text to Speech, Translation

As post-editing of machine translation (PEMT) is becoming one of the most dominant services offered by the language services industry (LSI), efforts are being made to support provision of this service with additional technology. We present text-to-speech (T2S) as a potential attention-raising technology for post-editors. Our study was conducted with university students and included both PEMT and error annotation of a creative text with and without T2S. Focusing on the error annotation data, our analysis finds that participants under-annotated fewer MT errors in the T2S condition compared to the silent condition. At the same time, more over-annotation was recorded. Finally, annotation performance corresponds to participants’ attitudes towards using T2S.
Post-editing in Automatic Subtitling: A Subtitlers’ perspective
Authors: Alina Karakanta, Luisa Bentivogli, Mauro Cettolo, Matteo Negri, Marco Turchi
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Translation

Recent developments in machine translation and speech translation are opening up opportunities for computer-assisted translation tools with extended automation functions. Subtitling tools are recently being adapted for post-editing by providing automatically generated subtitles, and featuring not only machine translation, but also automatic segmentation and synchronisation. But what do professional subtitlers think of post-editing automatically generated subtitles? In this work, we conduct a survey to collect subtitlers’ impressions and feedback on the use of automatic subtitling in their workflows. Our findings show that, despite current limitations stemming mainly from speech processing errors, automatic subtitling is seen rather positively and has potential for the future.
Working with Pre-translated Texts: Preliminary Findings from a Survey on Post-editing and Revision Practices in Swiss Corporate In-house Language Services
Authors: Sabrina Girletti
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Translation

With the arrival of neural machine translation, the boundaries between revision and post-editing (PE) have started to blur (Koponen et al., 2020). To shed light on current professional practices and provide new pedagogical perspectives, we set up a survey-based study to investigate how PE and revision are carried out in professional settings. We received 86 responses from corporate translators working at 26 different corporate in-house language services in Switzerland. Although the differences between the two activities seem to be clear for in-house linguists, our findings show that they tend to use the same reading strategies when working with human-translated and machine-translated texts.
Dynamic Adaptation of Neural Machine-Translation Systems Through Translation Exemplars
Authors: Arda Tezcan
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Domain Adaptation, Machine Translation, NMT, Translation

This project aims to study the impact of adapting neural machine translation (NMT) systems through translation exemplars, determine the optimal similarity metric(s) for retrieving informative exemplars, and, verify the usefulness of this approach for domain adaptation of NMT systems.
Language I/O Solution for Multilingual Customer Support
Authors: Diego Bartolome, Chris Jacob
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6

We describe the multilingual customer solution by Language I/O in this paper. With data security and confidentiality ensured by the ISO 27001 certification, global corporations can turn monolingual customer support agents into efficient multilingual brand ambassadors in less than 24 hours. Our solution supports more than 100 languages.
Towards Readability-Controlled Machine Translation of COVID-19 Texts
Authors: Fernando Alva-Manchego, Matthew Shardlow
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Text Simplification, Translation

This project investigates the capabilities of Machine Translation models for generating translations at varying levels of readability, focusing on texts related to COVID-19. Whilst it is possible to automatically translate this information, the resulting text may contain specialised terminology, or may be written in a style that is difficult for lay readers to understand. So far, we have collected a new dataset with manual simplifications for English and Spanish sentences in the TICO-19 dataset, as well as implemented baseline pipelines combining Machine Translation and Text Simplification models.
DeBiasByUs: Raising Awareness and Creating a Database of MT Bias
Authors: Joke Daems, Janiça Hackenbuchner
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Translation

This paper presents the project initiated by the BiasByUs team resulting from the 2021 Artificially Correct Hackaton. We briefly explain our winning participation in the hackaton, tackling the challenge on ‘Database and detection of gender bi-as in A.I. translations’, we highlight the importance of gender bias in Machine Translation (MT), and describe our pro-posed solution to the challenge, the cur-rent status of the project, and our envi-sioned future collaborations and re-search.
MultitraiNMT Erasmus+ project: Machine Translation Training for multilingual citizens (multitrainmt.eu)
Authors: Mikel L. Forcada, Pilar Sánchez-Gijón, Dorothy Kenny, Felipe Sánchez-Martínez, Juan Antonio Pérez Ortiz, Riccardo Superbo, Gema Ramírez Sánchez, Olga Torres-Hostench, Caroline Rossi
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, NMT, Translation

The MultitraiNMT Erasmus+ project has developed an open innovative syl-labus in machine translation, focusing on neural machine translation (NMT) and targeting both language learners and translators. The training materials include an open access coursebook with more than 250 activities and a pedagogical NMT interface called MutNMT that allows users to learn how neural machine translation works. These materials will allow students to develop the technical and ethical skills and competences required to become informed, critical users of machine translation in their own language learn-ing and translation practice. The pro-ject started in July 2019 and it will end in July 2022.
EMBEDDIA project: Cross-Lingual Embeddings for Less- Represented Languages in European News Media
Authors: Senja Pollak, Andraž Pelicon
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Keyword Extraction

EMBEDDIA project developed a range of resources and methods for less-resourced EU languages, focusing on applications for media industry, including keyword extraction, comment moderation and article generation.
Trados-to-Translog-II: Adding Gaze and Qualitivity data to the CRITT TPR-DB
Authors: Masaru Yamada, Takanori Mizowaki, Longhui Zou, Michael Carl
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Translation

The CRITT (Center for Research and Innovation in Translation and Translation Technology) provides a Translation Process Research Database (TPR-DB) and a rich set of summary tables and tools that help to investigate translator behavior. In this paper, we describe a new tool in the TPR-DB that converts Trados Studio keylogging data (Qualitivity) into Translog-II format and adds the converted data to the CRITT TPR-DB. The tool is also able to synchronize with the output of various eye-trackers. We describe the components of the new TPR-DB tool and highlight some of the features that it produces in the TPR-DB tables.
Writing in a second Language with Machine translation (WiLMa)
Authors: Margot Fonteyne, Maribel Montero Perez, Joke Daems, Lieve Macken
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Translation

The WiLMa project aims to assess the effects of using machine translation (MT) tools on the writing processes of second language (L2) learners of varying proficiency. Particular attention is given to individual variation in learners’ tool use.
Europeana Translate: Providing multilingual access to digital cultural heritage
Authors: Eirini Kaldeli, Mercedes García-Martínez, Antoine Isaac, Paolo Sebastiano Scalia, Arne Stabenau, Iván Lena Almor, Carmen Grau Lacal, Martín Barroso Ordóñez, Amando Estela, Manuel Herranz
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Translation

Europeana Translate is a project funded under the Connecting European Facility with the objective to take advantage of state-of-the-art machine translation in order to increase the multilinguality of resources in the cultural heritage domain
The PASSAGE project : Standard German Subtitling of Swiss German TV content
Authors: Pierrette Bouillon, Johanna Gerlach, Jonathan Mutal, Marianne Starlander
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Translation

We present the PASSAGE project, which aims at automatic Standard German subtitling of Swiss German TV content. This is achieved in a two step process, beginning with ASR to produce a normalised transcription, followed by translation into Standard German. We focus on the second step, for which we explore different approaches and contribute aligned corpora for future research.
MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages
Authors: Marta Bañón, Miquel Esplà-Gomis, Mikel L. Forcada, Cristian García-Romero, Taja Kuzman, Nikola Ljubešić, Rik van Noord, Leopoldo Pla Sempere, Gema Ramírez-Sánchez, Peter Rupnik, Vít Suchomel, Antonio Toral, Tobias van der Werff, Jaume Zaragoza
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6

We introduce the project “MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages”, funded by the Connecting Europe Facility, which is aimed at building monolingual and parallel corpora for under-resourced European languages. The approach followed consists of crawling large amounts of textual data from carefully selected top-level domains of the Internet, and then applying a curation and enrichment pipeline. In addition to corpora, the project will release successive versions of the free/open-source web crawling and curation software used.
MT-Pese: Machine Translation and Post-Editese
Authors: Sheila Castilho, Natália Resende
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Translation

This paper introduces the MT-Pese project, which aims at researching the post-editese phenomena in machine translated texts. We describe a range of experiments performed in order to gauge the effect of post-editese in dif-ferent domains, backtranslation, and quality.
A Quality Estimation and Quality Evaluation Tool for the Translation Industry
Authors: Elena Murgolo, Javad PourMostafa Roshan Sharami, Dimitar Shterionov
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Management, Sentence, Translation

With the increase in machine translation (MT) quality over the latest years, it has now become a common practice to integrate MT in the workflow of language service providers (LSPs) and other actors in the translation industry. With MT having a direct impact on the translation workflow, it is important not only to use high-quality MT systems, but also to understand the quality dimension so that the humans involved in the translation workflow can make informed decisions. The evaluation and monitoring of MT output quality has become one of the essential aspects of language technology management in LSPs’ workflows. First, a general practice is to carry out human tests to evaluate MT output quality before deployment. Second, a quality estimate of the translated text, thus after deployment, can inform post editors or even represent post-editing effort. In the former case, based on the quality assessment of a candidate engine, an informed decision can be made whether the engine would be deployed for production or not. In the latter, a quality estimate of the translation output can guide the human post-editor or even make rough approximations of the post-editing effort. Quality of an MT engine can be assessed on document- or on sentence-level. A tool to jointly provide all these functionalities does not exist yet. The overall objective of the project presented in this paper is to develop an MT quality assessment (MTQA) tool that simplifies the quality assessment of MT engines, combining quality evaluation and quality estimation on document- and sentence- level.
MTee: Open Machine Translation Platform for Estonian Government
Authors: Toms Bergmanis, Marcis Pinnis, Roberts Rozis, Jānis Šlapiņš, Valters Šics, Berta Bernāne, Guntars Pužulis, Endijs Titomers, Andre Tättar, Taido Purason, Hele-Andra Kuulmets, Agnes Luhtaru, Liisa Rätsep, Maali Tars, Annika Laumets-Tättar, Mark Fishel
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Document Translation, Grammatical Error Correction, Machine Translation, Translation

We present the MTee project - a research initiative funded via an Estonian public procurement to develop machine translation technology that is open-source and free of charge. The MTee project delivered an open-source platform serving state-of-the-art machine translation systems supporting four domains for six language pairs translating from Estonian into English, German, and Russian and vice-versa. The platform also features grammatical error correction and speech translation for Estonian and allows for formatted document translation and automatic domain detection. The software, data and training workflows for machine translation engines are all made publicly available for further use and research.
Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components
Authors: Raúl Vázquez, Michele Boggia, Alessandro Raganato, Niki A. Loppi, Stig-Arne Grönroos, Jörg Tiedemann
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, NMT, Translation

We describe the enhancement of a multilingual NMT toolkit developed as part of the FoTran project. We devise our modular attention-bridge model, which connects language-specific components through a shared network layer. The system now supports distributed training over many nodes and GPUs in order to substantially scale up the number of languages that can be included in a modern neural translation architecture. The model enables the study of emerging language-agnostic representations and also provides a modular toolkit for efficient machine translation.
InDeep × NMT: Empowering Human Translators via Interpretable Neural Machine Translation
Authors: Gabriele Sarti, Arianna Bisazza
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, NMT, Translation

Neural machine translation (NMT) systems are nowadays essential components of professional translation workflows. Consequently, human translators are increasingly working as post-editors for machine-translated content. The NWO-funded InDeep project aims to empower users of Deep Learning models of text, speech, and music by improving their ability to interact with such models and interpret their behaviors. In the specific context of translation, we aim at developing new tools and methodologies to improve prediction attribution, error analysis, and controllable generation for NMT systems. These advances will be evaluated through field studies involving professional translators to assess gains in efficiency and overall enjoyability of the post-editing process.
QUARTZ: Quality-Aware Machine Translation
Authors: José G.C. de Souza, Ricardo Rei, Ana C. Farinha, Helena Moniz, André F. T. Martins
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Translation

This paper presents QUARTZ, QUality-AwaRe machine Translation, a project led by Unbabel which aims at developing machine translation systems that are more robust and produce fewer critical errors. With QUARTZ we want to enable machine translation for user-generated conversational content types that do not tolerate critical errors in automatic translations.
POLENG MT: An Adaptive MT Platform
Authors: Artur Nowakowski, Krzysztof Jassem, Maciej Lison, Kamil Guttmann, Miko Pokrywka
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Document Translation, Translation

We introduce POLENG MT, an MT platform that may be used as a cloud web application or as an on-site solution. The platform is capable of providing accurate document translation, including the transfer of document formatting between the input document and the output document. The main feature of the on-site version is dedicated customer adaptation, which consists of training on specialized texts and applying forced terminology translation according to the user’s needs.
plain X - AI Supported Multilingual Video Workflow Platform
Authors: Carlos Amaral, Peggy van der Kreeft
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Translation

The plain X platform is a toolbox for multilingual adaptation, for video, audio, and text content. The software is a 4-in-1 tool, combining several steps in the adaptation process, i.e., transcription, translation, subtitling, and voice-over, all automatically generated, but with a high level of editorial control. Users can choose which translation engine is used (e.g., MS Azure, Google, DeepL) depending on best performance. As a result, plain X enables a smooth semi-automated production of subtitles or voice-over, much faster than with older, manual workflows. The software was developed out of EU research projects and has recently been rolled out for professional use. It brings Artificial Intelligence (AI) into the multilingual media production process, while keeping the human in the loop.
DELA Project: Document-level Machine Translation Evaluation
Authors: Sheila Castilho
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Document Level Machine Translation, Machine Translation, Position, Translation

This paper presents the results of the DELA Project. We describe the testing of context span for document-level evaluation, construction of a document-level corpus, and context position, as well as the latest developments of the project when looking at human and automatic evaluation metrics for document-level evaluation.
Background Search for Terminology in STAR MT Translate
Authors: Giorgio Bernardinello, Judith Klein
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Marketing, Translation

When interested in an internal web ap-plication for MT, corporate customers always ask how reliable terminology will be in their translations. Coherent vocabulary is crucial in many aspects of corporate translations, such as doc-umentation or marketing. The main goal every MT provider would like to achieve is to fully integrate the cus-tomer’s terminology into the model, so that the result does not need to be edit-ed, but this is still not always guaran-teed. Besides, a web application like STAR MT Translate allows our cus-tomers to use – integrated within the same page – different generic MT pro-viders which were not trained with customer-specific data. So, as a prag-matic approach, we decided to in-crease the level of integration between WebTerm and STAR MT Translate, adding to the latter more terminological information, with which the user can post-edit the translation if needed.
Sign Language Translation: Ongoing Development, Challenges and Innovations in the SignON Project
Authors: Dimitar Shterionov, Mirella De Sisto, Vincent Vandeghinste, Aoife Brady, Mathieu De Coster, Lorraine Leeson, Josep Blat, Frankie Picron, Marcello Paolo Scipioni, Aditya Parikh, Louis ten Bosh, John O’Flaherty, Joni Dambre, Jorn Rijckaert
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Sign Language Translation, Translation

The SignON project (www.signon-project.eu) focuses on the research and development of a Sign Language (SL) translation mobile application and an open communications framework. SignON rectifies the lack of technology and services for the automatic translation between signed and spoken languages, through an inclusive, humancentric solution which facilitates communication between deaf, hard of hearing (DHH) and hearing individuals. We present an overview of the current status of the project, describing the milestones reached to date and the approaches that are being developed to address the challenges and peculiarities of Sign Language Machine Translation (SLMT).
A Machine Translation-Powered Chatbot for Public Administration
Authors: Dimitra Anastasiou, Anders Ruge, Radu Ion, Svetlana Segărceanu, George Suciu, Olivier Pedretti, Patrick Gratz, Hoorieh Afkari
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Chatbot, Machine Translation, Translation

This paper is about a multilingual chatbot developed for public administration within the CEF funded project ENRICH4ALL. We argue for multi-lingual chatbots empowered through MT and discuss the integration of the CEF eTranslation service in a chatbot solution.