Tips for Training Post-editors

A good quality Machine Translation engine relies on the quality of the bilingual data used to train it. For most MT users, this bilingual data can be translated by humans, or it can be fully post-edited MT output. In both cases, the quality of the data will influence the engines quality. 
Selçuk Özcan, Transistent’s Co-founder will discuss the differences and give some tips for successful post-editing.Selçuk Özcan, Transistent’s Co-founder has given KantanMT permission to publish his blog post on Translation Quality. This post was originally published in Dragosfer and on the GALA Blog website.

We have entered a new age, and a new technology has come into play: Machine Translation (MT). It’s globally accepted that MT systems dramatically increase productivity but it’s a hard struggle to integrate this technology into your production process. Apart from handling the engine building and optimizing procedures, you have to transform your traditional workflow:

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The traditional roles of the linguists (translators, editors, reviewers etc.) are reconstructed and converged to find a suitable place in this new, innovative workflow. The emerging role is called ‘post-edit’ and the linguists assigned to this role are called ‘post-editors’. You may want to recruit some willing linguists for this role, or persuade your staff to adopt a different point of view. But whatever the case may be, some training sessions are a must.

What are covered in training sessions?

1. Basic concepts of MT systems

Post-editors should have a notion of the dynamics of MT systems. It is important to focus on the system that is utilized (RBMT/SMT/Hybrid). For widely used SMT systems, it’s necessary for them to know:

  • how the systems behave
  • the functions of the Translation Model and Language Model*
  • input (given set of data) and output (raw MT output) relationship
  • what changes in different domains

* It’s not a must to give detailed information about that topics but touching on the issue will make a difference in determining the level of technical backgrounds of candidates. Some of the candidates may be included in testing team.

2. The characteristics of raw MT output

Post-editors should know the factors affecting MT output. On the other hand, the difference between working on fuzzy TM systems and with SMT systems has to be mentioned during a proper training session. Let’s try to figure out what to be given:

  • MT process is not the ‘T’ of the TEP workflow and raw MT output is not the target text expected to be output of ‘T’ process.
  • In the earlier stages of SMT engines, the output quality varies depending on the project’s dynamics and errors are not identical. As the system improves quality level becomes more even and consistent within the same domain.
  • There may be some word or phrase gaps in the systems’ pattern mappings. (Detecting these gaps is one of the main responsibilities of testing team but a successful post-editor must be informed about the possible gaps.)

3. Quality issues

This topic has two aspects: defining required target (end product) quality, and evaluation and estimation of output quality. The first one gives you the final destination and the second one makes you know where you are.

Required quality level is defined according to the project requirements but it mostly depends on target audience and intended usage of the target text. It seems similar to the procedure in TEP workflow. However, it’s slightly different; engine improvement plan should also be considered while defining the target quality level. Basically, this parameter is classified into two groups: publishable andunderstandable quality.

Evaluation and estimation aspect is a little bit more complicated. The most challenging factor is standardizing measurement metrics. Besides, the tools and systems used to evaluate and estimate the quality level have some more complex features. If you successfully establish your quality system, then adversities become easier to cope with.

It’s post-editors’duty to apprehend the dynamics of MT quality evaluation, and the distinction between MT and HT quality evaluation procedures. Thus, they are supposed to be aware of the expected error patterns. It will be more convenient to utilize the error categorization with your well-trained staff (QE staff and post-editors).

4. Post-editing Technique

The fourth and the last topic is the key to success. It covers appropriate method and principles, as well as the perspective post-editors usually acquire. Post-edit technique is formed using the materials prepared for the previous topics and the data obtained from the above mentioned procedures, and it is separately defined for almost every individual customized engines.

The core rule for this topic is that post-edit technique, as a concept, is likely to be definitely differentiated from traditional edit and/or review technique(s). Post-editors are likely to be capable of:

  • reading and analyzing the source text, raw MT output and categorized and/or annotated errors as a whole.
  • making changes where necessary.
  • considering the post-edited data as a part of data set to be used in engine improvement, and performing his/her work accordingly.
  • applying the rules defined for the quality expectation levels.

As briefly described in topic #3, the distance between the measured output quality and required target quality may be seen as the post-edit distance. It roughly defines the post-editor’s tolerance and the extent to which he/she will perform his work. Other criterion allowing us to define the technique and the performance is the target quality group. If the target text is expected to be of publishable quality then it’s called full post-edit and otherwise light post-edit. Light & full post-edit techniques can be briefly defined as above but the distinction is not always so clear. Besides, under/over edit concepts are likely to be included to above mentioned issues. You may want to include some more details about these concepts in the post-editor training sessions; enriching the training materials with some examples would be a great idea!

About Selçuk Özcan

Selçuk Özcan has more than 5 years’ experience in the language industry and is a co-founder of Transistent Language Automation Services. He holds degrees in Mechanical Engineering and Translation Studies and has a keen interest in linguistics, NLP, language automation procedures, agile management and technology integration. Selçuk is mainly responsible for building high quality production models including Quality Estimation and deploying the ‘train the trainers’ model. He also teaches Computer-aided Translation and Total Quality Management at the Istanbul Yeni Yuzyil University, Translation & Interpreting Department.

Transistent Partner PR Image 320x320

 

Read More about KantanMT’s Partnership with Transistent in the official News Release, or if you are interested in joining the KantanMT Partner Program, contact Louise (info@kantanmt.com) for more details on how to get involved. 

 

 

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