AMTA 2023

Generative AI and the Future of Machine Translation

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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 included a keynote talk, expert panel discussions, presentations, tutorials for both beginners and more experienced practitioners, and demonstrations from technology providers.

Location

• Online

Links

• amtaweb.org/program-for-generative-ai-and-the-future-of-machine-translation/

Important Dates

Submission deadline	15 August
Notification of acceptance	15 September
Conference	08 November

Schedule

9:30	Networking
10:00	Introduction, housekeeping
10:15	Keynote Hype cycle, promising potential and everything in between: MT in the age of LLMs Christian Federmann
11:10	The Limits of Language AI Kirti Vashee
11:10	Evaluation of a Large Language Model for Speech-to-Text and Speech Translation Brendan Hatch, Evelyne Tzoukermann
11:35	Lost in GenAI, Found in MT Konstantin Savenkov
11:35	On-the-Fly Adaptation for Machine Translation using Large Language Models Kevin Duh, Suzanna Sia
12:00	“Self-driving” generative AI: How can we take our hands off the wheel? Adam Bittlingmayer
12:00	Domain and terminology adaptation with large language models: A comparative user study Yuri Balashov
12:25	Assessing the Accuracy and Uses of a Fine-Tuned LLM's Prediction of the Need for Human Intervention Serge Gladkoff
12:25	Anti-LM Decoding for Zero-shot In-Context Machine Translation** Suzanna Sia
12:45	🍴
13:15	Panel Automation and/or Augmentation: “What could AI do on its own?” becomes “What should we do and when? Donald Barabé, Markus Freitag, Giovanna Lester, Arle Lommel Moderator: Steve Richardson
14:05	The Promise of a Brand, New Day: Time to Switch to LLMs?** Alex Yanishevsky
14:05	The Future of MT: from Sequence Transduction to Machine-In-The-Loop Communication Across Languages Marine Carpuat
14:30	Proposal for a Joint Case Study by Google and Welocalize - Custom Translation** Sarah Weldon, Elaine O’Curran
14:30	Performance Evaluation on Human-Machine Teaming Augmented Machine Translation Enabled by GPT-4 Ming Quian
14:55	Leveraging ChatGPT machine translation capabilities for UGC Lucie Bovyn
14:55	Human-Centered Augmented Translation Sharon O'Brien
15:15	Mindful AI: Establishing an effective AI Ethics Board** Jay Marciano
15:15	Improving Machine Translation Quality with Contextual Prompts in Large Language Models Albert LLorens
15:35	☕️
15:50	Tutorial Prompt Engineering 101 Mei Chai Zheng
15:50	Tutorial Five daily linguistic tasks enhanced by generative AI + MT solutions Luciana Ramos
16:15	Learnings from a Year of Generative AI: Customers need Control, Customizability, Contextualization, Transparency, and Security** Chris Kränzler
16:15	Cultural Transcreation for East Asian Languages with LLMs Beatriz Silva, et al
16:40	From research to production, releasing AI tools and products at scale / Exploring Product Management for Machine Learning Products Jennifer Wong
16:40	Freely Available LLMs and Poetry Translation: A Game Changer? Natalia Resende
17:05	Embrace LLM: Opportunities and Challenges Martin Lei Xiao
17:05	Using LLMs to Automate Multilingual QAs Robert Brodowicz
17:30	Thanks and Introduce AMTA Thesis Award
17:45	Afterhours networking

Calls For Papers

Submissions

• 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)

Topics

• 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

Last updated on 16 September, 2023, from amtaweb.org/generative-ai-and-the-future-of-machine-translation.