The 60-page Slator Machine Translation Expert-in-the-Loop Report provides a comprehensive view on the interaction between human experts and AI in translation production.
The report shows how expert linguists and skilled project managers interact with machines, allowing AI-enabled language service providers (LSPs) to drive productivity and succeed in a hyper-competitive marketplace.
In this report, we investigate the different ways that AI and human experts interact throughout the translation process, including project management, translation, and vendor selection.
The report first examines the concept of human-machine interaction in translation, in which human experts and machines work together, taking on different roles with varying degrees of responsibility.
We examine the latest advances shaping and defining the relationship between human experts and AI in translation production — how they impact skills, workflows, processes, and technology. We also provide insights into how to optimize and harmonize this relationship.
Slator Machine Translation Expert-in-the-Loop Report
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Drawing on interviews with representatives from nine LSPs, the report highlights how expert-in-the-loop translation (aka MT-enabled workflows) has become the dominant translation method and continues to increase in adoption.
A handy, one-page table outlines the level of adoption of expert-in-the-loop translation workflows per vertical, provides examples of expert-in-the-loop translation in action for further reading, and identifies use cases for both non-MT and pure-MT workflows.
The report describes the technology that is involved in translation production as well as the role and characteristics of the human expert — typically (but not always) the post-editor.
The most substantial chapter of the report, Expert-in-the-Loop Translation Production, examines how AI can be applied to support not only translators within the translation / postediting task but also project managers (PMs) in managing expert-in-the-loop translation workflows, selecting vendors, and more.
The same chapter unpacks the different expert-in-the-loop translation methods and explores the application and potential of tools, such as quality estimation (QE), automatic postediting, and quality assessment (QA) in expert-in-the-loop workflows, as well as the suitability of translator interfaces for expert-in-the-loop translation production.
The final chapter, Expert-in-the-Loop Translation Pricing Models, outlines how technological advances brought about by MT, cloud computing, and AI-driven automation have affected and continue to influence industry pricing models.
Table of Contents
| Executive Summary | 4 |
| Introduction | 5 |
| Human Expert – Machine Interaction | 7 |
| Understand the concept of human-machine interaction and how machine translation has fundamentally transformed human-machine interaction in the translation workflow. | |
| Expert-in-the-Loop Translation: The Dominant Production Method | 8 |
| Find out the percentage adoption rate of expert-in-the-loop translation across the language services industry with insights from leading AI agencies. Access a one-page infographic illustrating adoption levels across 10 key verticals. | |
| Benefits and Challenges of Expert-in-the-Loop Translation | 13 |
| Read up on the key benefits of expert-in-the-loop translation and see how much leading LSPs have increased throughput and reduced costs. Gain insight into the main challenges that LSPs need to overcome — from technology investment and productivity tool design to translator sentiment. | |
| The Machine | 16 |
| Understand how AI has been injected into language services workflows — from machine translation to automated production — and discover the major drivers behind AI deployment. | |
| The Human Expert | 18 |
| Home in on the skill set and expertise needed by human experts in language services. Learn more about postediting training, subject-matter expertise, competencies and soft skills, as well as how to align job types with post-editor profiles. | |
| Expert-in-the-Loop Project Management | 24 |
| See how project managers (PMs) interact with AI to optimize the selection of linguists and MT engines. Find out how AI-driven content analytics inform PM decisions — and how the role of PMs now encompass AI gatekeeping and strategy. | |
| Expert-in-the-Loop Translation | 29 |
| Compare and contrast linear MT postediting with augmented translation workflows. | |
| Quality Estimation | 31 |
| Get up to speed on how MT quality estimation (QE) is used to optimize productivity. Delve deeper into a QE case study and discover more applications for QE in the language industry. | |
| Automatic Postediting | 34 |
| Find out how automatic postediting (APE) can reduce a post-editor’s workload, and how to mix QE with APE to further accelerate productivity. | |
| AI-driven Quality Assessment | 36 |
| Get a thorough rundown of the many benefits of combining quality assessment tools with AI. | |
| Interactive, Adaptive, and Context-Aware Machine Translation | 37 |
| Delve into three useful case studies to see how AI agencies employ interactive, adaptive, and context-aware machine translation. | |
| Translator Interfaces and Multi-Modal Postediting (MMPE) | 41 |
| Get a glimpse of the future of translator interfaces and multi-modal postediting, and see how post-editors can benefit from speech technologies. | |
| Expert-in-the-Loop Translation Pricing Models | 44 |
| Find out how per-word and per-hour editing rates are calculated and how other factors impact pricing. Gain insights on pricing from industry leaders. Access a one-page pricing model table that lists and explains eight different pricing models. | |
| References | 49 |
How to Use This Report
Slator’s easy-to-digest Machine Translation Expert-in-the-Loop Report offers the very latest industry and data analysis, providing language service providers and end clients the confidence to make informed and time critical decisions. It is a cost-effective, credible resource for busy professionals.
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