Machine Translation Summit (2019)


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Proceedings of Machine Translation Summit XVII: Research Track

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Proceedings of Machine Translation Summit XVII: Research Track
Mikel Forcada | Andy Way | Barry Haddow | Rico Sennrich

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Online Sentence Segmentation for Simultaneous Interpretation using Multi-Shifted Recurrent Neural Network
Xiaolin Wang | Masao Utiyama | Eiichiro Sumita

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Robust Document Representations for Cross-Lingual Information Retrieval in Low-Resource Settings
Mahsa Yarmohammadi | Xutai Ma | Sorami Hisamoto | Muhammad Rahman | Yiming Wang | Hainan Xu | Daniel Povey | Philipp Koehn | Kevin Duh

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Enhancing Transformer for End-to-end Speech-to-Text Translation
Mattia Antonino Di Gangi | Matteo Negri | Roldano Cattoni | Roberto Dessi | Marco Turchi

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Debiasing Word Embeddings Improves Multimodal Machine Translation
Tosho Hirasawa | Mamoru Komachi

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Translator2Vec: Understanding and Representing Human Post-Editors
António Góis | André F. T. Martins

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Domain Adaptation for MT: A Study with Unknown and Out-of-Domain Tasks
Hoang Cuong

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What is the impact of raw MT on Japanese users of Word: preliminary results of a usability study using eye-tracking
Ana Guerberof Arenas | Joss Moorkens | Sharon O’Brien

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MAGMATic: A Multi-domain Academic Gold Standard with Manual Annotation of Terminology for Machine Translation Evaluation
Randy Scansani | Luisa Bentivogli | Silvia Bernardini | Adriano Ferraresi

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Automatic error classification with multiple error labels
Maja Popovic | David Vilar

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Interactive-Predictive Neural Machine Translation through Reinforcement and Imitation
Tsz Kin Lam | Shigehiko Schamoni | Stefan Riezler

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An Intrinsic Nearest Neighbor Analysis of Neural Machine Translation Architectures
Hamidreza Ghader | Christof Monz

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Improving Neural Machine Translation Using Noisy Parallel Data through Distillation
Praveen Dakwale | Christof Monz

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Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation
Aizhan Imankulova | Raj Dabre | Atsushi Fujita | Kenji Imamura

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Improving Anaphora Resolution in Neural Machine Translation Using Curriculum Learning
Dario Stojanovski | Alexander Fraser

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Improving American Sign Language Recognition with Synthetic Data
Jungi Kim | Patricia O’Neill-Brown

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Selecting Informative Context Sentence by Forced Back-Translation
Ryuichiro Kimura | Shohei Iida | Hongyi Cui | Po-Hsuan Hung | Takehito Utsuro | Masaaki Nagata

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Memory-Augmented Neural Networks for Machine Translation
Mark Collier | Joeran Beel

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An Exploration of Placeholding in Neural Machine Translation
Matt Post | Shuoyang Ding | Marianna Martindale | Winston Wu

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Controlling the Reading Level of Machine Translation Output
Kelly Marchisio | Jialiang Guo | Cheng-I Lai | Philipp Koehn

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A Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation
Shuoyang Ding | Adithya Renduchintala | Kevin Duh

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The Impact of Preprocessing on Arabic-English Statistical and Neural Machine Translation
Mai Oudah | Amjad Almahairi | Nizar Habash

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Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation
Eva Vanmassenhove | Dimitar Shterionov | Andy Way

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Identifying Fluently Inadequate Output in Neural and Statistical Machine Translation
Marianna Martindale | Marine Carpuat | Kevin Duh | Paul McNamee

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Character-Aware Decoder for Translation into Morphologically Rich Languages
Adithya Renduchintala | Pamela Shapiro | Kevin Duh | Philipp Koehn

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Improving Translations by Combining Fuzzy-Match Repair with Automatic Post-Editing
John Ortega | Felipe Sánchez-Martínez | Marco Turchi | Matteo Negri

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Post-editing Productivity with Neural Machine Translation: An Empirical Assessment of Speed and Quality in the Banking and Finance Domain
Samuel Läubli | Chantal Amrhein | Patrick Düggelin | Beatriz Gonzalez | Alena Zwahlen | Martin Volk

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Post-editese: an Exacerbated Translationese
Antonio Toral


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Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks

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Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks
Mikel Forcada | Andy Way | John Tinsley | Dimitar Shterionov | Celia Rico | Federico Gaspari

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Competitiveness Analysis of the European Machine Translation Market
Andrejs Vasiļjevs | Inguna Skadiņa | Indra Sāmīte | Kaspars Kauliņš | Ēriks Ajausks | Jūlija Meļņika | Aivars Bērziņš

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Improving CAT Tools in the Translation Workflow: New Approaches and Evaluation
Mihaela Vela | Santanu Pal | Marcos Zampieri | Sudip Naskar | Josef van Genabith

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Hungarian translators’ perceptions of Neural Machine Translation in the European Commission
Ágnes Lesznyák

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Applying Machine Translation to Psychology: Automatic Translation of Personality Adjectives
Ritsuko Iwai | Daisuke Kawahara | Takatsune Kumada | Sadao Kurohashi

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Evaluating machine translation in a low-resource language combination: Spanish-Galician.
María Do Campo Bayón | Pilar Sánchez-Gijón

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MTPE in Patents: A Successful Business Story
Valeria Premoli | Elena Murgolo | Diego Cresceri

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User expectations towards machine translation: A case study
Barbara Heinisch | Vesna Lušicky

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Does NMT make a difference when post-editing closely related languages? The case of Spanish-Catalan
Sergi Alvarez | Antoni Oliver | Toni Badia

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Machine Translation in the Financial Services Industry: A Case Study
Mara Nunziatini

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Pre-editing Plus Neural Machine Translation for Subtitling: Effective Pre-editing Rules for Subtitling of TED Talks
Yusuke Hiraoka | Masaru Yamada

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Do translator trainees trust machine translation? An experiment on post-editing and revision
Randy Scansani | Silvia Bernardini | Adriano Ferraresi | Luisa Bentivogli

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On reducing translation shifts in translations intended for MT evaluation
Maja Popovic

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Comparative Analysis of Errors in MT Output and Computer-assisted Translation: Effect of the Human Factor
Irina Ovchinnikova | Daria Morozova

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A Comparative Study of English-Chinese Translations of Court Texts by Machine and Human Translators and the Word2Vec Based Similarity Measure’s Ability To Gauge Human Evaluation Biases
Ming Qian | Jessie Liu | Chaofeng Li | Liming Pals

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Translating Terminologies: A Comparative Examination of NMT and PBSMT Systems
Long-Huei Chen | Kyo Kageura

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NEC TM Data Project
Alexandre Helle | Manuel Herranz

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APE-QUEST
Joachim Van den Bogaert | Heidi Depraetere | Sara Szoc | Tom Vanallemeersch | Koen Van Winckel | Frederic Everaert | Lucia Specia | Julia Ive | Maxim Khalilov | Christine Maroti | Eduardo Farah | Artur Ventura

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PRINCIPLE: Providing Resources in Irish, Norwegian, Croatian and Icelandic for the Purposes of Language Engineering
Andy Way | Federico Gaspari

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iADAATPA Project: Pangeanic use cases
Mercedes García-Martínez | Amando Estela | Laurent Bié | Alexandre Helle | Manuel Herranz

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MICE
Joachim Van den Bogaert | Heidi Depraetere | Tom Vanallemeersch | Frederic Everaert | Koen Van Winckel | Katri Tammsaar | Ingmar Vali | Tambet Artma | Piret Saartee | Laura Katariina Teder | Artūrs Vasiļevskis | Valters Sics | Johan Haelterman | David Bienfait

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ParaCrawl: Web-scale parallel corpora for the languages of the EU
Miquel Esplà | Mikel Forcada | Gema Ramírez-Sánchez | Hieu Hoang

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Pivot Machine Translation in INTERACT Project
Chao-Hong Liu | Andy Way | Catarina Silva | André Martins

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Global Under-Resourced Media Translation (GoURMET)
Alexandra Birch | Barry Haddow | Ivan Tito | Antonio Valerio Miceli Barone | Rachel Bawden | Felipe Sánchez-Martínez | Mikel L. Forcada | Miquel Esplà-Gomis | Víctor Sánchez-Cartagena | Juan Antonio Pérez-Ortiz | Wilker Aziz | Andrew Secker | Peggy van der Kreeft

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Neural machine translation system for the Kazakh language
Ualsher Tukeyev | Zhandos Zhumanov

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Leveraging Rule-Based Machine Translation Knowledge for Under-Resourced Neural Machine Translation Models
Daniel Torregrosa | Nivranshu Pasricha | Maraim Masoud | Bharathi Raja Chakravarthi | Juan Alonso | Noe Casas | Mihael Arcan

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Bootstrapping a Natural Language Interface to a Cyber Security Event Collection System using a Hybrid Translation Approach
Johann Roturier | Brian Schlatter | David Silva Schlatter

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Improving Robustness in Real-World Neural Machine Translation Engines
Rohit Gupta | Patrik Lambert | Raj Patel | John Tinsley

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Surveying the potential of using speech technologies for post-editing purposes in the context of international organizations: What do professional translators think?
Jeevanthi Liyanapathirana | Pierrette Bouillon | Bartolomé Mesa-Lao

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Automatic Translation for Software with Safe Velocity
Dag Schmidtke | Declan Groves

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Application of Post-Edited Machine Translation in Fashion eCommerce
Kasia Kosmaczewska | Matt Train

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Morphological Neural Pre- and Post-Processing for Slavic Languages
Giorgio Bernardinello

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Large-scale Machine Translation Evaluation of the iADAATPA Project
Sheila Castilho | Natália Resende | Federico Gaspari | Andy Way | Tony O’Dowd | Marek Mazur | Manuel Herranz | Alex Helle | Gema Ramírez-Sánchez | Víctor Sánchez-Cartagena | Mārcis Pinnis | Valters Šics

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Collecting domain specific data for MT: an evaluation of the ParaCrawlpipeline
Arne Defauw | Tom Vanallemeersch | Sara Szoc | Frederic Everaert | Koen Van Winckel | Kim Scholte | Joris Brabers | Joachim Van den Bogaert

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Monolingual backtranslation in a medical speech translation system for diagnostic interviews - a NMT approach
Jonathan Mutal | Pierrette Bouillon | Johanna Gerlach | Paula Estrella | Hervé Spechbach

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Improving Domain Adaptation for Machine Translation withTranslation Pieces
Catarina Silva

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Raising the TM Threshold in Neural MT Post-Editing: a Case Study onTwo Datasets
Anna Zaretskaya

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Incremental Adaptation of NMT for Professional Post-editors: A User Study
Miguel Domingo | Mercedes García-Martínez | Álvaro Peris | Alexandre Helle | Amando Estela | Laurent Bié | Francisco Casacuberta | Manuel Herranz

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When less is more in Neural Quality Estimation of Machine Translation. An industry case study
Dimitar Shterionov | Félix Do Carmo | Joss Moorkens | Eric Paquin | Dag Schmidtke | Declan Groves | Andy Way


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bib (full) Proceedings of Machine Translation Summit XVII: Tutorial Abstracts

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Proceedings of Machine Translation Summit XVII: Tutorial Abstracts
Laura Rossi

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The unreasonable effectiveness of Neural Models in Language Decoding
Tony O'Dowd

This tutorial will provide an in-depth look at the experiments, jointly carried out by KantanMT and eBay during 2018, to determine which Neural Model delivers the best translation performance for eBay Customer Service content. It will lay out the timeline, process and mechanisms used to customise Neural MT models and how these were used in conjunction with Human Based evaluations to determine which approach to Neural MT provided the best translation outcomes.The tutorial will cover the following topics and methods:- Structural differences in Neural Networks and how they assist the language decoding process – RNN, CNN and TNN will be covered in detailed.- Customisation of Neural MT using the KantanMT Platform- Using MQM Framework for the evaluation and comparison of Translation Outputs and comparison to Human Translation- Collation and analysis of experimental findings in reaching our decision to standardise on Transformer type networks.Participants of the tutorial will get a clear understanding of Neural Model types and the differences, it will also cover how to customise these models and then how to set up a controlled experiment to determine translation performance.

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Challenge Test Sets for MT Evaluation
Maja Popović | Sheila Castilho

Most of the test sets used for the evaluation of MT systems reflect the frequency distribution of different phenomena found in naturally occurring data (”standard” or ”natural” test sets). However, to better understand particular strengths and weaknesses of MT systems, especially those based on neural networks, it is necessary to apply more focused evaluation procedures. Therefore, another type of test sets (”challenge” test sets, also called ”test suites”) is being increasingly employed in order to highlight points of difficulty which are relevant to model development, training, or using of the given system. This tutorial will be useful for anyone (researchers, developers, users, translators) interested in detailed evaluation and getting a better understanding of machine translation (MT) systems and models. The attendees will learn about the motivation and linguistic background of challenge test sets and a range of testing possibilities applied to the state-of-the-art MT systems, as well as a number of practical aspects and challenges.


A Deep Learning Curve for Post-Editing 2
Lena Marg | Alex> Yanishevsky

In the last couple of years, machine translation technology has seen major changes with the breakthrough of neural machine translation (NMT), a growing number of providers and translation platforms. Machine Translation generally is experiencing a peak in demand from translation buyers, thanks to Machine Learning and AI being omnipresent in the media and at industry events. At the same time, new models for defining translation quality are becoming more widely adopted. These changes have profound implications for translators, LSPs and translation buyers: translators have to adjust their post-editing approaches, while LSPs and translation buyers are faced with decisions on selecting providers, best approaches for updating MT systems, financial investments, integrating tools, and getting the timing for implementation right for an optimum ROI.In this tutorial on MT and post-editing we would like to continue sharing the latest trends in the field of MT technologies, and discuss their impact on post-editing practices as well as integrating MT on large, multi-language translation programs. We will look at tool compatibility, different use cases of MT and dynamic quality models, and share our experience of measuring performance.


Practical Statistics for Research in Machine Translation and Translation Studies
Antonio Toral

The tutorial will introduce a set of very useful statistical tests for conducting analyses in the research areas of Machine Translation (MT) and Translation Studies (TS). For each statistical test, the presenter will: 1) introduce it in the context of a common research example that pertains to the area of MT and/or TS 2) explain the technique behind the test and its assumptions 3) cover common pitfalls when the test is applied in research studies, and 4) conduct a hands-on activity so that attendees can put the knowledge acquired in practice straight-away. All examples and exercises will be in R. The following statistical tests will be covered: t-tests (both parametric and non-parametric), bootstrap resampling, Pearson and Spearman correlation coefficients, linear mixed-effects models.