pwc-archive/papers-with-abstracts huggingface dataset:
The Myth of Signing Avatars
Authors: John C. McDonald, Rosalee Wolfe, Eleni Efthimiou, Evita Fontinea, Frankie Picron, Davy Van Landuyt, Tina Sioen, Annelies Braffort, Michael Filhol, Sarah Ebling, Thomas Hanke, Verena Krausneker
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Position, Translation

Development of automatic translation between signed and spoken languages has lagged behind the development of automatic translation between spoken languages, but it is a common misperception that extending machine translation techniques to include signed languages should be a straightforward process. A contributing factor is the lack of an acceptable method for displaying sign language apart from interpreters on video. This position paper examines the challenges of displaying a signed language as a target in automatic translation, analyses the underlying causes and suggests strategies to develop display technologies that are acceptable to sign language communities.
AVASAG: A German Sign Language Translation System for Public Services (short paper)
Authors: Fabrizio Nunnari, Judith Bauerdiek, Lucas Bernhard, Cristina España-Bonet, Corinna Jäger, Amelie Unger, Kristoffer Waldow, Sonja Wecker, Elisabeth André, Stephan Busemann, Christian Dold, Arnulph Fuhrmann, Patrick Gebhard, Yasser Hamidullah, Marcel Hauck, Yvonne Kossel, Martin Misiak, Dieter Wallach, Alexander Stricker
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Sign Language Translation, Translation

This paper presents an overview of AVASAG; an ongoing applied-research project developing a text-to-sign-language translation system for public services. We describe the scientific innovation points (geometry-based SL-description, 3D animation and video corpus, simplified annotation scheme, motion capture strategy) and the overall translation pipeline.
Using Computer Vision to Analyze Non-manual Marking of Questions in KRSL
Authors: Anna Kuznetsova, Alfarabi Imashev, Medet Mukushev, Anara Sandygulova, Vadim Kimmelman
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8

This paper presents a study that compares non-manual markers of polar and wh-questions to statements in Kazakh-Russian Sign Language (KRSL) in a dataset collected for NLP tasks. The primary focus of the study is to demonstrate the utility of computer vision solutions for the linguistic analysis of non-manuals in sign languages, although additional corrections are required to account for biases in the output. To this end, we analyzed recordings of 10 triplets of sentences produced by 9 native signers using both manual annotation and computer vision solutions (such as OpenFace). We utilize and improve the computer vision solution, and briefly describe the results of the linguistic analysis.
Approaching Sign Language Gloss Translation as a Low-Resource Machine Translation Task
Authors: Xuan Zhang, Kevin Duh
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Sign Language Translation, Translation

A cascaded Sign Language Translation system first maps sign videos to gloss annotations and then translates glosses into a spoken languages. This work focuses on the second-stage gloss translation component, which is challenging due to the scarcity of publicly available parallel data. We approach gloss translation as a low-resource machine translation task and investigate two popular methods for improving translation quality: hyperparameter search and backtranslation. We discuss the potentials and pitfalls of these methods based on experiments on the RWTH-PHOENIX-Weather 2014T dataset.
Automatic generation of a 3D sign language avatar on AR glasses given 2D videos of human signers
Authors: Lan Thao Nguyen, Florian Schicktanz, Aeneas Stankowski, Eleftherios Avramidis
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, speech-recognition, Speech Recognition

In this paper we present a prototypical implementation of a pipeline that allows the automatic generation of a German Sign Language avatar from 2D video material. The presentation is accompanied by the source code. We record human pose movements during signing with computer vision models. The joint coordinates of hands and arms are imported as landmarks to control the skeleton of our avatar. From the anatomically independent landmarks, we create another skeleton based on the avatar’s skeletal bone architecture to calculate the bone rotation data. This data is then used to control our human 3D avatar. The avatar is displayed on AR glasses and can be placed virtually in the room, in a way that it can be perceived simultaneously to the verbal speaker. In further work it is aimed to be enhanced with speech recognition and machine translation methods for serving as a sign language interpreter. The prototype has been shown to people of the deaf and hard-of-hearing community for assessing its comprehensibility. Problems emerged with the transferred hand rotations, hand gestures were hard to recognize on the avatar due to deformations like twisted finger meshes.
Online Evaluation of Text-to-sign Translation by Deaf End Users: Some Methodological Recommendations (short paper)
Authors: Floris Roelofsen, Lyke Esselink, Shani Mende-Gillings, Maartje De Meulder, Nienke Sijm, Anika Smeijers
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Translation

We present a number of methodological recommendations concerning the online evaluation of avatars for text-to-sign translation, focusing on the structure, format and length of the questionnaire, as well as methods for eliciting and faithfully transcribing responses
Defining meaningful units. Challenges in sign segmentation and segment-meaning mapping (short paper)
Authors: Mirella De Sisto, Dimitar Shterionov, Irene Murtagh, Myriam Vermeerbergen, Lorraine Leeson
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Segmentation, Translation

This paper addresses the tasks of sign segmentation and segment-meaning mapping in the context of sign language (SL) recognition. It aims to give an overview of the linguistic properties of SL, such as coarticulation and simultaneity, which make these tasks complex. A better understanding of SL structure is the necessary ground for the design and development of SL recognition and segmentation methodologies, which are fundamental for machine translation of these languages. Based on this preliminary exploration, a proposal for mapping segments to meaning in the form of an agglomerate of lexical and non-lexical information is introduced.
Neural Translator Designed to Protect the Eastern Border of the European Union
Authors: Artur Nowakowski, Krzysztof Jassem
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Translation

This paper reports on a translation engine designed for the needs of the Polish State Border Guard. The engine is a component of the AI Searcher system, whose aim is to search for Internet texts, written in Polish, Russian, Ukrainian or Belarusian, which may lead to criminal acts at the eastern border of the European Union. The system is intended for Polish users, and the translation engine should serve to assist understanding of non-Polish documents. The engine was trained on general-domain texts. The adaptation for the criminal domain consisted in the appropriate translation of criminal terms and proper names, such as forenames, surnames and geographical objects. The translation process needs to take into account the rich inflection found in all of the languages of interest. To this end, a method based on constrained decoding that incorporates an inflected lexicon into a neural translation process was applied in the engine.
Corpus Creation and Evaluation for Speech-to-Text and Speech Translation
Authors: Corey Miller, Evelyne Tzoukermann, Jennifer Doyon, Elizabeth Mallard
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Speech-to-Text, Translation

The National Virtual Translation Center (NVTC) seeks to acquire human language technology (HLT) tools that will facilitate its mission to provide verbatim English translations of foreign language audio and video files. In the text domain, NVTC has been using translation memory (TM) for some time and has reported on the incorporation of machine translation (MT) into that workflow (Miller et al., 2020). While we have explored the use of speech-totext (STT) and speech translation (ST) in the past (Tzoukermann and Miller, 2018), we have now invested in the creation of a substantial human-made corpus to thoroughly evaluate alternatives. Results from our analysis of this corpus and the performance of HLT tools point the way to the most promising ones to deploy in our workflow.
Glossary functionality in commercial machine translation: does it help? A first step to identify best practices for a language service provider
Authors: Randy Scansani, Loïc Dugast
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: de-en, Machine Translation, Translation

Recently, a number of commercial Machine Translation (MT) providers have started to offer glossary features allowing users to enforce terminology into the output of a generic model. However, to the best of our knowledge it is not clear how such features would impact terminology accuracy and the overall quality of the output. The present contribution aims at providing a first insight into the performance of the glossary-enhanced generic models offered by four providers. Our tests involve two different domains and language pairs, i.e. Sportswear En–Fr and Industrial Equipment De–En. The output of each generic model and of the glossaryenhanced one will be evaluated relying on Translation Error Rate (TER) to take into account the overall output quality and on accuracy to assess the compliance with the glossary. This is followed by a manual evaluation. The present contribution mainly focuses on understanding how these glossary features can be fruitfully exploited by language service providers (LSPs), especially in a scenario in which a customer glossary is already available and is added to the generic model as is.
Selecting the best data filtering method for NMT training
Authors: Fred Bane, Anna Zaretskaya
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: NMT, Translation

Performance of NMT systems has been proven to depend on the quality of the training data. In this paper we explore different open-source tools that can be used to score the quality of translation pairs, with the goal of obtaining clean corpora for training NMT models. We measure the performance of these tools by correlating their scores with human scores, as well as rank models trained on the resulting filtered datasets in terms of their performance on different test sets and MT performance metrics.
Machine Translation Post-Editing (MTPE) from the Perspective of Translation Trainees: Implications for Translation Pedagogy
Authors: Dominika Cholewska
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Translation

This paper introduces data on translation trainees’ perceptions of the MTPE process and implications on training in this field. This study aims to analyse trainees’ performance of three MTPE tasks the English-Polish language pair and post-tasks interviews to determine the need to promote machine translation post-editing skills in educating translation students. Since very little information concerning MTPE training is available, this study may be found advantageous.
Field Experiments of Real Time Foreign News Distribution Powered by MT
Authors: Keiji Yasuda, Ichiro Yamada, Naoaki Okazaki, Hideki Tanaka, Hidehiro Asaka, Takeshi Anzai, Fumiaki Sugaya
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Abstractive Text Summarization, Articles, Machine Translation, Text Summarization

Field experiments on a foreign news distribution system using two key technologies are reported. The first technology is a summarization component, which is used for generating news headlines. This component is a transformer-based abstractive text summarization system which is trained to output headlines from the leading sentences of news articles. The second technology is machine translation (MT), which enables users to read foreign news articles in their mother language. Since the system uses MT, users can immediately access the latest foreign news. 139 Japanese LINE users participated in the field experiments for two weeks, viewing about 40,000 articles which had been translated from English to Japanese. We carried out surveys both during and after the experiments. According to the results, 79.3% of users evaluated the headlines as adequate, while 74.7% of users evaluated the automatically translated articles as intelligible. According to the post-experiment survey, 59.7% of users wished to continue using the system; 11.5% of users did not. We also report several statistics of the experiments.
Validating Quality Estimation in a Computer-Aided Translation Workflow: Speed, Cost and Quality Trade-off
Authors: Fernando Alva-Manchego, Lucia Specia, Sara Szoc, Tom Vanallemeersch, Heidi Depraetere
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Translation

In modern computer-aided translation workflows, Machine Translation (MT) systems are used to produce a draft that is then checked and edited where needed by human translators. In this scenario, a Quality Estimation (QE) tool can be used to score MT outputs, and a threshold on the QE scores can be applied to decide whether an MT output can be used as-is or requires human post-edition. While this could reduce cost and turnaround times, it could harm translation quality, as QE models are not 100% accurate. In the framework of the APE-QUEST project (Automated Post-Editing and Quality Estimation), we set up a case-study on the trade-off between speed, cost and quality, investigating the benefits of QE models in a real-world scenario, where we rely on end-user acceptability as quality metric. Using data in the public administration domain for English-Dutch and English-French, we experimented with two use cases: assimilation and dissemination. Results shed some light on how QE scores can be explored to establish thresholds that suit each use case and target language, and demonstrate the potential benefits of adding QE to a translation workflow.
Dealing with the Paradox of Quality Estimation
Authors: Sugyeong Eo, Chanjun Park, Hyeonseok Moon, Jaehyung Seo, Heuiseok Lim
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Sentence, Translation

In quality estimation (QE), the quality of translation can be predicted by referencing the source sentence and the machine translation (MT) output without access to the reference sentence. However, there exists a paradox in that constructing a dataset for creating a QE model requires non-trivial human labor and time, and it may even requires additional effort compared to the cost of constructing a parallel corpus. In this study, to address this paradox and utilize the various applications of QE, even in low-resource languages (LRLs), we propose a method for automatically constructing a pseudo-QE dataset without using human labor. We perform a comparative analysis on the pseudo-QE dataset using multilingual pre-trained language models. As we generate the pseudo dataset, we conduct experiments using various external machine translators as test sets to verify the accuracy of the results objectively. Also, the experimental results show that multilingual BART demonstrates the best performance, and we confirm the applicability of QE in LRLs using pseudo-QE dataset construction methods.
Small-Scale Cross-Language Authorship Attribution on Social Media Comments
Authors: Benjamin Murauer, Gunther Specht
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Authorship Attribution, Language Modelling, Machine Translation, Translation

Cross-language authorship attribution is the challenging task of classifying documents by bilingual authors where the training documents are written in a different language than the evaluation documents. Traditional solutions rely on either translation to enable the use of single-language features, or language-independent feature extraction methods. More recently, transformer-based language models like BERT can also be pre-trained on multiple languages, making them intuitive candidates for cross-language classifiers which have not been used for this task yet. We perform extensive experiments to benchmark the performance of three different approaches to a smallscale cross-language authorship attribution experiment: (1) using language-independent features with traditional classification models, (2) using multilingual pre-trained language models, and (3) using machine translation to allow single-language classification. For the language-independent features, we utilize universal syntactic features like part-of-speech tags and dependency graphs, and multilingual BERT as a pre-trained language model. We use a small-scale social media comments dataset, closely reflecting practical scenarios. We show that applying machine translation drastically increases the performance of almost all approaches, and that the syntactic features in combination with the translation step achieve the best overall classification performance. In particular, we demonstrate that pre-trained language models are outperformed by traditional models in small scale authorship attribution problems for every language combination analyzed in this paper.
Morphologically-Guided Segmentation For Translation of Agglutinative Low-Resource Languages
Authors: William Chen, Brett Fazio
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, NMT, Segmentation, Translation

Neural Machine Translation (NMT) for Low Resource Languages (LRL) is often limited by the lack of available training data, making it necessary to explore additional techniques to improve translation quality. We propose the use of the Prefix-Root-Postfix-Encoding (PRPE) subword segmentation algorithm to improve translation quality for LRLs, using two agglutinative languages as case studies: Quechua and Indonesian. During the course of our experiments, we reintroduce a parallel corpus for Quechua-Spanish translation that was previously unavailable for NMT. Our experiments show the importance of appropriate subword segmentation, which can go as far as improving translation quality over systems trained on much larger quantities of data. We show this by achieving state-of-the-art results for both languages, obtaining higher BLEU scores than large pre-trained models with much smaller amounts of data.
Love Thy Neighbor: Combining Two Neighboring Low-Resource Languages for Translation
Authors: John E. Ortega, Richard Alexander Castro Mamani, Jaime Rafael Montoya Samame
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Transfer Learning, Translation, Vocal Bursts Valence Prediction

Low-resource languages sometimes take on similar morphological and syntactic characteristics due to their geographic nearness and shared history. Two low-resource neighboring languages found in Peru, Quechua and Ashaninka, can be considered, at first glance, two languages that are morphologically similar. In order to translate the two languages, various approaches have been taken. For Quechua, neural machine transfer-learning has been used along with byte-pair encoding. For Ashaninka, the language of the two with fewer resources, a finite-state transducer is used to transform Ashaninka texts and its dialects for machine translation use. We evaluate and compare two approaches by attempting to use newly-formed Ashaninka corpora for neural machine translation. Our experiments show that combining the two neighboring languages, while similar in morphology, word sharing, and geographical location, improves Ashaninka– Spanish translation but degrades Quechua–Spanish translations.
A Comparison of Different NMT Approaches to Low-Resource Dutch-Albanian Machine Translation
Authors: Arbnor Rama, Eva Vanmassenhove
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Articles, Machine Translation, NMT, Translation

Low-resource languages can be understood as languages that are more scarce, less studied, less privileged, less commonly taught and for which there are less resources available (Singh, 2008; Cieri et al., 2016; Magueresse et al., 2020). Natural Language Processing (NLP) research and technology mainly focuses on those languages for which there are large data sets available. To illustrate differences in data availability: there are 6 million Wikipedia articles available for English, 2 million for Dutch, and merely 82 thousand for Albanian. The scarce data issue becomes increasingly apparent when large parallel data sets are required for applications such as Neural Machine Translation (NMT). In this work, we investigate to what extent translation between Albanian (SQ) and Dutch (NL) is possible comparing a one-to-one (SQ↔AL) model, a low-resource pivot-based approach (English (EN) as pivot) and a zero-shot translation (ZST) (Johnson et al., 2016; Mattoni et al., 2017) system. From our experiments, it results that the EN-pivot-model outperforms both the direct one-to-one and the ZST model. Since often, small amounts of parallel data are available for low-resource languages or settings, experiments were conducted using small sets of parallel NL↔SQ data. The ZST appeared to be the worst performing models. Even when the available parallel data (NL↔SQ) was added, i.e. in a few-shot setting (FST), it remained the worst performing system according to the automatic (BLEU and TER) and human evaluation.
Manipuri-English Machine Translation using Comparable Corpus
Authors: Lenin Laitonjam, Sanasam Ranbir Singh
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Translation, Unsupervised Machine Translation

Unsupervised Machine Translation (MT) model, which has the ability to perform MT without parallel sentences using comparable corpora, is becoming a promising approach for developing MT in low-resource languages. However, majority of the studies in unsupervised MT have considered resource-rich language pairs with similar linguistic characteristics. In this paper, we investigate the effectiveness of unsupervised MT models over a Manipuri-English comparable corpus. Manipuri is a low-resource language having different linguistic characteristics from that of English. This paper focuses on identifying challenges in building unsupervised MT models over the comparable corpus. From various experimental observations, it is evident that the development of MT over comparable corpus using unsupervised methods is feasible. Further, the paper also identifies future directions of developing effective MT for Manipuri-English language pair under unsupervised scenarios.
EnKhCorp1.0: An English–Khasi Corpus
Authors: Sahinur Rahman Laskar, Abdullah Faiz Ur Rahman Khilji Darsh Kaushik, Partha Pakray, Sivaji Bandyopadhyay
Date: 2021-08-01 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Machine Translation, Translation

In machine translation, corpus preparation is one of the crucial tasks, particularly for lowresource pairs. In multilingual countries like India, machine translation plays a vital role in communication among people with various linguistic backgrounds. There are available online automatic translation systems by Google and Microsoft which include various languages which lack support for the Khasi language, which can hence be considered lowresource. This paper overviews the development of EnKhCorp1.0, a corpus for English–Khasi pair, and implemented baseline systems for EnglishtoKhasi and KhasitoEnglish translation based on the neural machine translation approach.