Post-editing is a necessary step in the Machine Translation workflow, but the role is still largely misunderstood. Language Service Providers (LSPs) are now experimenting more with the best practices for post-editing in the workflow. The lack of consistent training and reluctance within the industry to accept importance of the role are linked to the post-editors motivation. KantanMT looks at some of the more conventional attitudes towards motivation and their application to post-editing.
What is motivation and what studies have been done so far?
Understanding the concept of motivation has been a hot topic in many areas of organisation theory. Studies in the area really began to kick off with their application in the workplace, opening doors for pioneers to understand how employees could be motivated to do more work, and do better work.
- Abraham Maslow and his well-known ‘Hierarchy of Needs’ indicates a person’s motivations are based on their position in the hierarchy pyramid.
- Frederick Herzberg’s ‘two Factor Theory’ or Herzberg’s motivation-hygiene theory suggests professional activities like; professional acknowledgement, achievements and work responsibility, or job satisfiers have a positive effect on motivation.
- Douglas McGregor used a black and white approach to motivation in his ‘Theory X and Theory Y’. He grouped employees into two categories; those who will only do the minimum and those who will push themselves.
As development of theories continued…
- John Adair came up with the ‘fifty-fifty theory’ . According to the fifty-fifty theory, motivation is fifty percent the responsibility of the employee and fifty percent outside the employee’s control.
Even more recently, in 2010
- Teresa Amabile and Steven Kramer carried out a study on the motivation levels of employees in a variety of settings. Their findings, suggest ‘Progress’ as the top performance motivator identified from an analysis of approx. 12,000 diary entries, daily ratings of motivation and emotions from hundreds of study participants.
To understand post-editor motivation we can combine the top performance motivator; progress with fifty-fifty theory.
Progress is a healthy motivator in the post-editing profession, it can help Localization Project Managers understand and encourage post-editor satisfaction and motivation. But while progress can be deemed an external factor, if we apply Adair’s ‘fifty-fifty’ rule, post-editors are also at least fifty percent responsible for their own motivation.
Post-editing as a profession is still only finding its feet, TAUS carried out a study in 2010 on the post editing practices of global LSPs. The study showed that, while post-editing is becoming a standard activity in the translation workflow it only accounts for a minor share of LSP business volume. This indicates that post-editors see their role as one of lesser importance because the industry views it as a role of lesser importance.
This attitude in the industry is highlighted by the lack of industry standards for post-editing best practices. Without evaluation practices to train post-editors and improve the post-editing process, post-editors are not making progress. This quite naturally is demotivating for the post-editor.
How to motivate post-editors
The first step in motivating post-editors is to recognise their role as autonomous to the role of a translator. The best post-editors are those, who are at least bilingual with some form of linguistic training, like a translator. Linguistic training is a major asset for editing the Machine Translated output.
TAUS offer a comparison of the translation process versus the post-editing process, highlighting the differences in the post-editing and translation processes.
One process is not more complicated that the other, only different. Translators, translate internally, while post-editors make “snap editing decisions” based on client requirements. As LSPs recognise these differences, they can successfully motivate their post-editors by providing them with the most suitable support, and work environment.
Progress as a Motivator
Translators make good post-editors, they have the linguistic ability to understand both the source and target texts, and if they enjoy editing or proof-reading, then the post-editing role will suit them. The right training is also important, if post-editors are trained properly they will become more aware of potential improvements to the workflow.
These improvements or ideas can be a great boost to post-editor motivation, if implemented the post-editor can take on more responsibility, which helps improve the translation workflow. A case where this could be applied is; if the post-editor is made responsible for updating the language assets used to retrain a Machine Translation system, they can take ownership and become responsible for the output quality rather than just post-editing Machine Translation output in isolation.
Fixing repetitive errors, can be frustrating for anyone, not just post-editors. But if they are responsible for the output quality, understand the system and can control the rules used to reduce these repetitive errors, they will experience motivation through progress.
This is only the tip of the iceberg on what motivates post-editors, each post-editor is different and how they feel about the role, whether it is just ‘another job’ or a major step in their career all play a part. The key is to provide proper training, foster an environment where post-editors can make progress by positively contributing to the role.
Translators often take pride and ownership of their translations, post-editors should also have the opportunity to take pride in their work, as it is their skills and experience that make it ‘publishable’ or even ‘fit for purpose’ quality.
Repetitive errors like diacritic marks or capitalisation can be easily fixed using KantanMT’s Post-Editing Automation (PEX) rules. PEX rules allow repetitive errors in a Machine Translation engine to be easily fixed using a ‘find and replace’ tool. These rules can be checked on a sample of the text by using the PEX Rule Editor.
The post-editor can correct repetitive errors during post-editing process, so the same errors don’t appear in future MT output, giving them responsibility over the Machine Translation engines quality.