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How to Define a Successful NLP?

Successful NLP is straightforward from the end user’s perspective:

  • Knowing what I want to say and what to do — like a natural person.
  • Making the right actions.
  • Giving the correct response.

The so-called artificial intelligence, whose benchmark is human, mainly refers to the certain field of human-computer interaction. The real difficulty of NLP lies in the analysis process — the text analysis does not mean simply recognizing the content of some words or sentences. The purpose of the machine is to identify the intention, however, the human intention is divergent.

Based on the above reasons, while customers make NLP engines or applications, they often delineate some application scenarios in advance, such as some of the most common alarm clocks, calendars, Q&A, or some shared search content, for example, on-demand videos or restaurants search. This way, AI can understand, analyze, and respond to common intentions in relative scenarios. For content outside the scope, it will say: “sorry, I did not understand what you said.”

Tone problem

The tone is part of sentiment analysis. It is an essential aspect, and it is said to be a difficult point. If you try to speak to a voice assistant in a different tone, such as a rhetorical question, it is highly likely to give you a wrong answer.

How to solve the tone problem?

One solution is to use scalable training data so as to cover a variety of scenarios, such as similar content with different tones, and different meanings represented by different contexts. However, it is hard to get it all at first.

The different stage has different goals. After all, the most basic stage should be consolidated, and then various complex dimensions can be gradually superimposed. As far as we know, normal conversation tone, average speaking speed are all prevalent content. In conclusion, it is crucial to train the most conventional scenes at first to pursue gradually higher complexity.

High demand for scalable and customized dataset

At present, the demand for the highest quality AI training data in various industries is urgent. AI is implemented in various fields, such as education, law, intelligent driving, banking, finance, etc. Each field has requirements for subdivision and specialization.

Among them, in particular, traditional enterprises with intelligent transformation and technology enterprises need the assistance of training data service providers with rich project experience to help sort out the data labeling instruction and to obtain more suitable data. The use of high-quality data in special scenarios reduces the research and development cycle, accelerates the implementation process, and helps enterprises to make faster and better intelligent transformations.

In the process of in-depth industrial landing, there is still a gap between artificial intelligence technology and enterprise needs. The core goal of enterprise users is to use artificial intelligence technology to achieve business growth. Actually, artificial intelligence technology itself cannot directly solve all the business needs. It needs to create products and services that can be implemented on a large scale based on specific business scenarios and goals.

What we need to be clear is for AI companies and the entire industry, data annotation is an important part of the realization of artificial intelligence. The accuracy and efficiency of the labeled data affect the final result of the artificial intelligence algorithm model.

NLP case study — Korean dialogue collection

Project description: 2-person 240 hours of Korean conversation collection

Our company is responsible for the collection of Korean dialogue and carries out the quality inspection service according to the guideline. Each conversation lasts 5–15 minutes. Each group would make a recording of 6 hours.

We gather a certain number of Korean native speakers to record the conversation in the form of telephone calls. The conversation is on multiple topics, including aviation, agriculture, delivery services, finance, banking, health, etc.

ByteBridge, a human-powered and ML-powered data labeling tooling platform

ByteBridge is a data labeling SaaS platform with robust tools and real-time workflow management. It provides high-quality training data for the machine learning industry.


  • ML-assisted capacity can help reduce human errors by automatically pre-labeling
  • The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
  • Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
  • All results are thoroughly assessed and verified by a human workforce and machine
ByteBridge: a Human-powered and ML-powered Data Labeling SaaS Platform

In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.


A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.

NLP Service

We provide different types of NLP in E-commerce, Retail, Search engines, Social Media, etc. Our service includes Voice Classification, Sentiment Analysis, Text Recognition and Text Classification(Chatbot Relevance).

Partnered with over 30 different language-speaking communities across the globe, ByteBridge now provides data collection and text annotation services covering languages such as English, Chinese, Spanish, Korean, Bengali, Vietnamese, Indonesian, Turkish, Arabic, Russian and more.


If you need data labeling and collection services, please have a look at, the clear pricing is available.

Please feel free to contact us:




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A data labeling platform with robust tools for real-time workflow management, providing high-quality training data with efficiency. —

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