Link Search Menu Expand Document

AMTA2020

Conference of the Association of Machine Translation in the Americas


The 14th biennial conference of the Association of Machine Translation in the Americas (AMTA 2020) was hosted online from 5 October to 9 October, 2020.

Proceedings

Schedule

Day 1

Monday, 5 October, 20220304

   
12:00 - 20:00 Opening Reception
Networking Area

Day 2

Tuesday, 6 October, 2020

   
8:00 - 11:00 Tutorial: Quick-Start Guide to Understanding and Working with Machine Translation
Adam Wooten
  Tutorial: NMT Domain Adaptation Techniques
Yasmin Moslem
08:00 - 10:10 Workshop: Virtual Workshop on the Impact of Machine Translation (iMpacT 2020)
Sharon O’Brien, Michel Simard
10:30 - 12:30 Workshop: Virtual Workshop on the Impact of Machine Translation (iMpacT 2020) (continued)
Sharon O’Brien, Michel Simard
12:00 - 15:00 Tutorial: Human Parity Demystified - How Models and Data Achieve Improved Translation Quality
Christian Federmann
  Workshop: 1st Workshop on Post-Editing in Modern-Day Translation (PEMDT1)
John E. Ortega, Marcello Federico, Constantin Orasan, Maja Popovic

Day 3

Wednesday, 7 October, 2020

   
08:00 - 08:10 Opening
08:10 - 08-50 Keynote: MT Developments in the European Union
Andy Way
08:50 - 09:00 🕑
09:00 - 09:30 Commercial User Track: Operationalizing MT quality estimation
Miklos Urban, Maribel Rodríguez Molina
  Research Track: A New Approach to Parameter-Sharing in Machine Translation
Benyamin Ahmadnia, Bonnie Dorr
09:30 - 10:00 Commercial User Track: In search of an acceptability/unacceptability threshold in machine translation post-editing automated metrics
Lucía Guerrero
  Research Track: Investigation of Transformer-based Latent Attention Models for Neural Machine Translation
Parnia Bahar, Nikita Makarov, Hermann Ney
10:00 - 10:30 Commercial User Track: A Survey of Qualitative Error Analysis for Neural Machine Translation Systems
Denise Díaz, James Cross, Vishrav Chaudhary, Ahmed Kishky, Philipp Koehn
  Research Track: Machine Translation with Unsupervised Length-Constraints
Jan Niehues
10:30 - 11:00 Commercial User Track: COMET - Deploying a New State-of-the-art MT Evaluation Metric in Production
Craig Stewart, Ricardo Rei, Catarina Farinha, Alon Lavie
  Research Track: Constraining the Transformer NMT Model with Heuristic Grid Beam Search
Guodong Xie, Andy Way
11:00 - 11:30 Demo 1 - Intento
11:30 - 12:00 Demo 2 - Systran
12:00 - 12:50 Keynote: Faithful NLG in an era of Ethical Awareness: Opportunities for MT
Mona Diab
12:50 - 13:00 🕑
13:00 - 13:30 Commercial User Track: Scaling up automatic translation for software: reduction of post-editing volume with well-defined customer impact
Dag Schmidtke
  Research Track: Machine Translation System Selection from Bandit Feedback
Jason Naradowsky, Xuan Zhang, Kevin Duh
13:30 - 14:00 Commercial User Track: Auto MT Quality Prediction Solution and Best Practice
Martin Lei Xiao, York Jin
  Research Track: Generative latent neural models for automatic word alignment
Anh Khoa Ngo Ho, Franc ̧ois Yvon
14:00 - 14:30 Commercial User Track: A language comparison of human evaluation & quality estimation
Silvio Picinini, Adam Bittlingmayer
  Research Track: The Impact of Indirect Machine Translation on Sentiment Classification
Alberto Poncelas, Pintu Lohar, James Hadley, Andy Way
14:30 - 15:00 Commercial User Track: Machine translation quality across demographic dialectal variation in Social Media
Adithya Renduchintala, Dmitriy Genzel
  Research Track: Towards Handling Compositionality in Low-Resource Bilingual Word Induction
Viktor Hangya, Alexander Fraser
15:00 - 15:10 🕑
15:10 - 16:15 Student Mentoring Session

Day 4

Thursday, 8 October, 2020

   
08:00 - 08:10 Opening
08:10 - 08-50 Keynote: Factor 1000: Better MT, more content, and what we can do with it
Chris Wendt
08:50 - 09:00 🕑
09:00 - 09:30 Commercial User Track: Making the business case for adopting MT
Rodrigo Cristina
  Research Track: The OpenNMT Neural Machine Translation Toolkit: 2020 Edition
Guillaume Klein, François Hernandez, Vincent Nguyen, Jean Senellart
09:30 - 10:00 Commercial User Track: Flexible Customization of a Single Neural Machine Translation System with Multi-dimensional Metadata Inputs
Evgeny Matusov, Patrick Wilken, Christian Herold
  Research Track: The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020
Tobias Domhan, Michael Denkowski, David Vilar, Xing Niu, Felix Hieber, Kenneth Heafield
10:00 - 10:30 Commercial User Track: **Enhance CX with Neural Machine Translation Technology
Rubén Martínez-Domínguez, Matíss Rikters, Artūrs Vasiļevskis, Mārcis Pinnis, Paula Reichenberg
  Research Track: THUMT: An Open-Source Toolkit for Neural Machine Translation
Zhixing Tan, Jiacheng Zhang, Xuancheng Huang, Gang Chen, Shuo Wang, Maosong Sun, Huanbo Luan, Yang Liu
10:30 - 11:00 Commercial User Track: Machine Translation Hype, Crash-Tested by Translation Students
Jon Ritzdorf
  Research Track: Panel on Open Source NMT Toolkit Development
Moderator: Matt Post
11:00 - 11:30 Demo 3 - Amazon
11:30 - 12:00 Demo 4 - XTM
12:00 - 12:50 Keynote: Navigating Change: Keys to implementing language technology in government
Danielle Silverman
12:50 - 13:00 🕑
13:00 - 13:30 Commercial User Track: Use MT to Simplify and Speed Up Your Alignment for TM Creation
Judith Klein
  Research Track: Successful Tech Transfer of MT Research in Government
Kathy Baker
13:30 - 14:00 Commercial User Track: Selection of MT System in Translation Workflows
Aleš Tamchyna
  Research Track: Plugging into Trados: Augmenting Translation in the Enclave
Corey Miller, Chiara Higgins, Paige Havens, Steven Van Guilder, Rodney Morris, Danielle Silverman
  Commercial User Track: Beyond MT: Opening Doors for an NLP Pipeline
Alex Yanishevsky
  Research Track: PEMT for the Public Sector: Discovery, Scoping, and Delivery
Konstantine Boukhvalov, Eileen Block
  Commercial User Track: Building Multi-Purpose MT Portfolio
Konstantin Savenkov
15:00 - 15:10 🕑
15:10 - 16:30 AMTA Business Meeting

Day 5

Friday, 9 October, 2020

   
07:50 - 08:00 Opening
08:00 - 08:50 Keynote: *Using language technology to enable two-way communication in humanitarian assistance
Eric Paquin
08:50 - 09:00 🕑
09:00 - 09:30 Government Track: A Tale of Eight Countries or the EU Council Presidency Translator in Retrospect (slides)
Mārcis Pinnis, Toms Bergmanis, Kristīne Metuzāle, Valters Šics, Artūrs Vasiļevskis, Andrejs Vasiļjevs
09:30 - 10:00 Government Track: American Sign Language (ASL) to English Machine Translation
Patricia O’Neill-Brown
09:00 - 10:00 Student Mentoring Session
10:00 - 10:30 Government Track: Why is it so Hard to Develop Comparable Translation Evaluations and How Can Standards Help?
Jennifer DeCamp
  Commercial User Track: Simultaneous Speech Translation in Google Translate
Jeff Pitman
10:30 - 11:00 Government Track: Using Contemporary US Government Data to Train Custom MT for COVID-19
Achim Ruopp
  Commercial User Track: Understanding Challenges to Enterprise Machine Translation Adoption
Bart Mączyński
11:00 - 11:30 Demo 5 - Facebook
11:30 - 12:00 Demo 5 - CustomMT
12:00 - 12:50 Keynote: Research stories from Google Translate’s Transcribe Mode
Colin Cherry
12:50 - 13:00 🕑
  Commercial User Track: Lexically Constrained Decoding for Sequence Generation
Tony O’Dowd
  Research Track: Dynamic Masking for Improved Stability in Online Spoken Language Translation
Yuekun Yao, Barry Haddow
  Commercial User Track: Building Salesforce Neural Machine Translation System
Kazuma Hashimoto, Raffaella Buschiazzo, Caiming Xiong, Teresa Marxhall
  Research Track: On Target Segmentation for Direct Speech Translation
Mattia A. Di Gangi, Marco Gaido, Matteo Negri, Marco Turchi
  Commercial User Track: **Interactive Adaptation of Neural MT on Commercial Datasets
Patrick Simianer, Joern Wuebker, John DeNero
  Research Track: Domain Robustness in Neural Machine Translation
Mathias Müller, Annette Rios, Rico Sennrich
  Commercial User Track: **Enabling New MT Post-Editing Scenarios with Continuous Localization
Igor Afanasyev
  Research Track: Low-Resource NMT: an Empirical Study on the Effect of Rich Morphological Word Segmentation on Inuktitut
Tan Ngoc, Fatiha Sadat

Edit this article →

Machine Translate is created and edited by contributors like you!

Learn more about contributing →

Licensed under CC-BY-SA-4.0.

Cite this article →