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There are two main approaches to performing annotation: the "manual" method and the "automated by machine" method. AI development requires a large amount of training data, and the annotation work necessary to create this data inevitably results in a large volume of work. On the other hand, at this point, complete automation of annotation work is difficult, making it a task that still requires human intervention. Why is manual annotation work necessary? Furthermore, what are the specific benefits unique to manual work? This article will introduce these points.
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1. Why manual annotation is necessary
The manual method is an approach where label information is assigned to text, audio, and images by hand. Because detailed work can be performed by humans, there are advantages in terms of quality and the range of possible responses. Annotation work is necessary for the advanced undertaking of AI development, but at this point, manual annotation is still unavoidable. Why is that?
Annotation automation is still in development
Various research and development projects are underway to automate annotation tasks. For example, in the automation of annotation work for rare intractable diseases by Yamamoto et al. (*1), they are examining annotation technology that extracts information necessary to estimate diseases from case reports and links it with controlled vocabulary.
Also, in the automation of annotation work for classifying traditional pattern images by Kagamikawa et al. (*2), efforts are being made to automate the annotation work for automatically identifying patterns such as plum, cherry blossom, and rhombus drawn on Ise katagami (paper stencils).
On the other hand, including these studies, the current situation is that many studies target specific domains. It can be said that we have not yet reached the point where annotation work can be automated for general purposes.
*1 Yamamoto "Development of automatic annotation technology for Japanese case reports"
*2 Kagamikawa "Pre-training with natural and fractal images for automating traditional pattern annotation"
Complete automation by machines is difficult, and supporting manual work is more realistic
The method of automating by machine is an approach using work automation via scripts or methods using generative models, which are AI technologies. At this point, these technologies are not mature and can only be used in limited areas.
As such, complete automation of annotation is difficult at this time. Annotation work requires human intervention, and the role of machines can be described as supporting the work performed by humans.
Therefore, the current way to proceed with annotation is for humans to skillfully use tools that support the annotation work, making manual annotation more efficient and of higher quality.
In fact, various tools to support annotation work have been developed. For example, tools are provided with functions such as semi-automatically identifying objects so that annotators can work efficiently, or performing tracking for videos. By making good use of these, annotation work can be carried out efficiently.
At Nextremer, AI specialists with a wealth of experience in creating high-quality training data provide annotation services. If you are considering outsourcing annotation even slightly, we offer free consultations, so please feel free to contact us at any time.
Human intervention is required to ensure practical quality
In this situation, while utilizing tools, humans ultimately take responsibility for the quality of annotation. If the skills of the annotators performing the task are insufficient, or if the members supervising the annotation work lack expertise in annotation or project management skills, the quality of the annotation cannot be ensured.
When outsourcing annotation work, human skills ultimately become an important factor. Consideration should be given to outsourcing to a company that is well-versed in the latest trends in annotation and can proceed with work efficiently, including the use of tools.
2. What are the strengths of manual annotation?
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Below, we explain the strengths of manual annotation.
Quality assurance
A benefit of manual annotation is that it is easy to ensure quality. To improve quality, it is also possible to devise ways to proceed with the annotation work. Specifically, the following measures can be taken in manual annotation:
| ① Participation of members proficient in annotation work ② Establishment of annotation rules that can ensure annotation quality ③ Strengthening the check system, such as conducting double-checks |
In this way, the strength of manual annotation is that quality can be improved through various measures.
Flexibility
Another advantage of manual annotation is the ability to perform work flexibly. Annotation work can be carried out in response to various requirements and needs. For example, subjective judgment is one of them. In cases where emotions felt toward a certain sentence are assigned as label information, efforts can be made to provide emotional information by averaging subjective judgments by multiple people.
Also, due to the circumstances of AI development, needs may arise to change annotation specifications in the middle of a project. In such cases, the strength of manual annotation is the ability to respond flexibly.
3. Summary
In this article, we explained the reasons why manual annotation work is necessary and its benefits. Annotation work, which is necessary for AI development, is currently an undertaking that largely depends on human power. Therefore, human skill is important for performing high-quality annotation work. If you cannot secure personnel for annotation work within your company, you also need to consider hiring an outsourcing partner with the necessary skills.
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