AMTA 2023

Generative AI and the Future of Machine Translation

Generative AI and the Future of Machine Translation will take place online on 8 November 2023. It is organised by AMTA.

Generative AI and the Future of Machine Translation will focus on advances in cross-lingual technology and processes that leverage Large Language Models. Moving beyond the explosion of hype that began with the general availability of ChatGPT in November 2022, this event is intended as a forum for a thoughtful exchange of ideas informed by a year of successes and failures in applying Large Language Models to the challenges of cross-lingual communication and data processing.

Generative AI and the Future of Machine Translation will include a keynote talk, expert panel discussions, presentations, tutorials for both beginners and more experienced practitioners, and demonstrations from technology providers.

Important dates

31 July New: 15 August Submission deadline
15 September Notification of acceptance
8 November Conference


09:30 - 10:00 Networking  
10:00 - 10:15 Introduction, housekeeping  
10:15 - 11:00 Keynote
Christian Federmann
11:10 - 11:30 The Limits of Language AI
Kirti Vashee
Channel 1
11:10 - 11:30 Evaluation of a Large Language Model for Speech-to-Text and Speech Translation
Brendan Hatch, Evelyne Tzoukermann
Channel 2
11:35 - 11:55 Lost in GenAI, Found in MT
Konstantin Savenkov
Channel 1
11:35 - 11:55 On-the-Fly Adaptation for Machine Translation using Large Language Models
Kevin Duh, Suzanna Sia
Channel 2
12:00 - 12:20 “Self-driving” generative AI: How can we take our hands off the wheel?
Adam Bittlingmayer
Channel 1
12:00 - 12:20 Domain and terminology adaptation with large language models: A comparative user study
Yuri Balashov
Channel 2
12:25 - 12:45 Assessing the Accuracy and Uses of a Fine-Tuned LLM’s Prediction of the Need for Human Intervention
Serge Gladkoff
Channel 1
12:25 - 12:45 Anti-LM Decoding for Zero-shot In-Context Machine Translation
Suzanna Sia
Channel 2
12:45 - 13:15 🍴  
13:15 - 14:00 Panel
To be determined
14:05 - 14:25 The Promise of a Brand, New Day: Time to Switch to LLMs?
Alex Yanishevsky
Channel 1
14:05 - 14:25 The Future of MT: from Sequence Transduction to Machine-In-The-Loop Communication Across Languages
Marine Carpuat
Channel 2
14:30 - 14:50 Proposal for a Joint Case Study by Google and Welocalize - Custom Translation
Sarah Weldon, Lena Marg
Channel 1
14:30 - 14:50 Performance Evaluation on Human-Machine Teaming Augmented Machine Translation Enabled by GPT-4
Ming Quian
Channel 2
14:55 - 15:10 Leveraging ChatGPT machine translation capabilities for UGC
Lucie Bovyn
Channel 1
14:55 - 15:10 Human-Centered Augmented Translation
Sharon O’Brien
Channel 2
15:15 - 15:35 Mindful AI: Establishing an effective AI Ethics Board
Jay Marciano
Channel 1
15:15 - 15:35 Improving Machine Translation Quality with Contextual Prompts in Large Language Models
Albert LLorens
Channel 2
15:35 - 15:50 ☕️  
15:50 - 16:10 Tutorial
Prompt Engineering 101
Mei Chai Zheng
Channel 1
15:50 - 16:10 Tutorial
Five daily linguistic tasks enhanced by generative AI + MT solutions
Luciana Ramos
Channel 2
16:15 - 16:35 Learnings from a Year of Generative AI: Customers need Control, Customizability, Contextualization, Transparency, and Security
Chris Kränzler
Channel 1
16:15 - 16:35 Cultural Transcreation for East Asian Languages with LLMs
Beatriz Silva, et al
Channel 2
16:40 - 17:00 From research to production, releasing AI tools and products at scale / Exploring Product Management for Machine Learning Products
Jennifer Wong
Channel 1
16:40 - 17:00 Freely Available LLMs and Poetry Translation: A Game Changer?
Natalia Resende
Channel 2
17:05 - 17:25 Embrace LLM: Opportunities and Challenges
Martin Lei Xiao
Channel 1
17:05 - 17:25 Using LLMs to Automate Multilingual QAs
Robert Brodowicz
Channel 2
17:30 - 17:45 Thanks and Introduce AMTA Thesis Award  
17:45 - 19:00 Afterhours networking  

Last updated on 16 September, 2023, from

Calls for papers


  • 20-minute talks (15 minute presentations, 5 minutes for questions)
  • 40-minute tutorials (practical description or exercises concerning the use of large language models, generative AI, machine translation, or related tools, processes and technologies for cross-lingual tasks)


  • Open-source large Llanguage models for translation, transcreation, and other cross-lingual use cases
  • Adaptation and customization of large language models for cross-lingual use cases
  • Combining narrow and large models to improve performance of specific tasks
  • Augmenting machine translation systems with generative AI
  • Training Data
  • Output quality and confidence scoring for cross-lingual tasks
  • Challenges to adopting large language models in cross-lingual use cases: Responsible deployment and regulatory considerations of generative AI
  • Generative AI use for professional translation
  • Business Cases
  • Future research directions

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