Results of the summarizing process for the arXiv paper: 1803.08409v1

In the chapter titled "Quality expectations of machine translation," author Andy Way explores the deployment and evaluation of machine translation (MT) and its potential as a productivity enhancer for human translators. Millions of people rely on MT for various use-cases on a daily basis, however there are still skeptics who question its usefulness. Way addresses this issue by discussing the current deployment of MT, how its output is evaluated and ways in which its quality can be improved. He emphasizes that there is no longer a single 'gold standard' measure of quality in MT; instead, the context in which it is deployed must be taken into consideration when assessing its expected lifespan or "shelf-life". As MT technology advances, it becomes essential to enhance evaluation methods and adapt them to different deployment scenarios. This chapter provides valuable insights into the utility and potential of MT while acknowledging the need for ongoing improvements in quality assessment. By understanding these factors, stakeholders can make informed decisions about incorporating MT into their translation workflows and maximizing its benefits.

• Author Andy Way explores the deployment and evaluation of machine translation (MT) and its potential as a productivity enhancer for human translators
• Millions of people rely on MT for various use-cases on a daily basis
• There are skeptics who question the usefulness of MT
• Current deployment of MT and how its output is evaluated are discussed
• Quality in MT is no longer measured by a single 'gold standard'; context must be considered when assessing its expected lifespan or "shelf-life"
• Evaluation methods need to be enhanced and adapted to different deployment scenarios as MT technology advances
• This chapter provides insights into the utility and potential of MT while acknowledging the need for ongoing improvements in quality assessment
• Stakeholders can make informed decisions about incorporating MT into their translation workflows and maximizing its benefits.

Exploring the Quality Expectations of Machine Translation

The world of translation is rapidly evolving, and machine translation (MT) has become an increasingly popular tool for both professional translators and everyday users. However, there are still many skeptics who question its usefulness and reliability. In his chapter “Quality expectations of machine translation” from the book Machine Translation: Its Scope and Limits, author Andy Way explores the deployment and evaluation of MT in order to address these concerns.

Current Deployment of MT

Way begins by discussing how MT is currently deployed in various use-cases. He emphasizes that it is no longer used as a stand-alone tool; instead, it is often integrated into existing workflows or combined with other technologies such as post-editing tools to improve accuracy. This integration allows MT to be more useful than ever before, but also increases the complexity of evaluating its output quality.

Evaluating Output Quality

Way then turns his attention to how MT output can be evaluated accurately. He notes that there is no single “gold standard” measure for quality assessment; instead, different metrics must be taken into consideration depending on the context in which MT is deployed. For example, if a translator needs to quickly translate a large volume of text for a low-stakes project, then speed may be more important than accuracy when assessing output quality. On the other hand, if an organization requires high-quality translations for legal documents or marketing materials, then accuracy should take precedence over speed when evaluating results.

Improving Quality Assessment

Finally, Way discusses ways in which quality assessment methods can be improved as technology advances. He suggests that stakeholders should focus on adapting evaluation methods to different deployment scenarios rather than relying on one universal metric for all contexts. Additionally, he emphasizes that ongoing research into better understanding user expectations will help ensure that stakeholders make informed decisions about incorporating MT into their workflows and maximizing its benefits while minimizing potential risks associated with inaccurate translations or misunderstandings due to language barriers.

In conclusion, this chapter provides valuable insights into the utility and potential of machine translation while acknowledging the need for ongoing improvements in quality assessment methods as technology continues to evolve at an unprecedented rate. By understanding these factors more deeply through research such as this article by Andy Way , stakeholders can make informed decisions about incorporating MT into their translation workflows and maximizing its benefits while minimizing any associated risks .