How do you balance the trade-off between speed, cost, and accuracy in linguistic data annotation?

Balancing speed, cost, and accuracy in linguistic data annotation starts with clear goals and scope. Understand what you want your NLP system to achieve and the data it requires. Define the linguistic features and categories relevant to your purpose. How much data is needed, and how will you collect it? This clarity guides the design of your annotation scheme and helps choose the right tools. Balancing trade-offs means optimizing annotation speed without compromising accuracy, keeping costs in check. As a Language Service Provider, aligning these factors ensures efficient and effective linguistic data annotation for robust NLP system development.

In short it just means that you need to have clear annotation guidelines which should represent the volume, variety of data to be annotated or the subjectivity in the data that might occur and process that should be followed in such a case. It also includes who will supervise, how many annotators, by when this should be completed to be considered successful. If not delivered on time, even the 100% accurate data might not deliver ROI in some use cases.

Attention to detail can never be overlooked. With NLP in particular, pitch, tone, cadence, and inflection can make all of the difference when the content is either being compiled, or being disseminated. One needs to stay in one's lane if they are using NLP as a basis for a chatbot reproduction. Localization and consistency are obviously important factors here as well. Since content can be super- localized, it may be possible to link a user's ISP or geolocation to help with the proper vocabulary curated to convey its message, compiling and building data streams which are most appropriate for (exact) consumer.

The best way to contribute is to refuse point blank to give anything of use to AI models, which are just monetised by giant corporations and used by people for writing shortcuts.

In my career as a translator, I've learned the importance of defining clear goals and scope for every project. One time, while translating for a high-profile client, I realized that understanding the project's purpose and the expected audience was crucial. This approach helped me tailor my translations to fit cultural nuances and specific client needs, ensuring accuracy and relevance. Balancing speed with precision, is also vital. My experience taught me that using the right tools and methods, like CAT tools for quality assurance, can significantly enhance efficiency without compromising quality. Understanding your audience, is key to making translations engaging and informative.

The annotation guidelines and project's cost feasibility decide what annotation method can be chosen Even less but urgent timeline might be better off with manual approach just in an excel for the kind of subjective task which otherwise might take good time and effort to adapt to automation and even tool setup. On the other hand, a long term and huge volume of data would be better off with a scenario where it could start with manual on a tool along with initial guidelines and continue adapt to the more automation and less human in the loop for the cases easier to automate.

Its not very straightforward though because once chosen, then it might take good amount of effort to move to another tool because of learning curve, configuration setup, communication and buying procedure. Now if you are just experimenting and have negligible data, you might prefer to use a tool open source or commercial with free limits. Open source doesn't always mean free, you might need engineering team effort to host and maintain reliability. However, when you are clear of continuous or large work then prefer to use a commercial especially when you know there is a chance of custom workflows and processes. It's just that you have to negotiate with the vendor such as Labellerr, super annotate depending upon the industry

While executing these projects in the past and recently, i can say that data consistency is a metric easy to measure and hard to fix in a highly subjective setting. Person 1 might look at a tomato and say that it is ripened while other might say that it is not. Even it might be possible that majority might say it incorrect and so on. For this there are multiple tools and techniques that we have devised which make it feasible to not just achieve but maintain it even at changing data velocity and veracity