Early incarnations of chatbots date back to the 1950s, but today, they live all around us.

Around 67% of global consumers had some interaction with a chatbot in 2021, and the industry is forecasted to reach $102.29 billion by 2027, representing a CAGR of 34.75%. 

‍NLP and deep learning have propelled a new generation of advanced human-like chatbots that can register and understand sentiments, intentions, named entities, and other components of human conversation. 

Many businesses and organizations are adopting or employing chatbots for everything from customer service, onboarding, internal communications, personal services, and research purposes. 

Chatbot Adoption 

Businesses and organizations of all shapes are adding chatbots to their communications channels. Not only do chatbots help lean and streamline customer service, but they also provide answers to customer queries quickly and efficiently. Moreover, the latest generations of chatbots are remarkably intelligible, assisting users in everything from booking holidays to learning languages and diagnosing medical issues.

When it comes to building chatbots, one of the chief bottlenecks is obtaining training data. While humans often take communication for granted, language is incredibly dynamic and data-rich. It consists of grammar, syntax, vocabulary, and many more interlinked elements. 

Making that data available to a chatbot is a tricky task that machine learning practitioners have been working to overcome. While deep learning accelerates the chatbot learning process, it still relies on good-quality data. 

Recent Developments in NLP 

The development of bidirectional transformer machine learning models has led to significant developments in NLP. Transformer models combine unsupervised, self-supervised, and supervised machine learning methods to pre-train models on large datasets before refining them to specific use cases. 

Google’s BERT was integrated into the company’s search and indexing algorithms in 2019 and 2020, taking the NLP industry by storm. Today, BERT, RoBERTa, DistilBERT, GPT-3, and XLNet are the most powerful NLP models around, and many feature similar architectures to BERT. 

The BERT models are pre-trained on a general corpus (generally the BookCorpus and Wikipedia), consisting of over 3 billion words. Once pre-trained, these modern NLP models can be fed smaller quantities of labeled text to fine-tune them for ultra-specific use cases. 

In essence, the process would look something like this: 

• Training a BERT model on a large corpus and/or domain-specific language
• Then training with “weak labels” based on keywords within that domain 
• And finally, using a high-quality, well-labeled human-validated dataset to fine-tune to the use case

Labeling Data For Chatbots

Highly sophisticated NLP models are pre-trained on a large corpus. Then, you can feed them labeled inputs to add additional layers. 

There are many well-known open-source datasets for building chatbots. You find some here and an overview of finding and using data to train chatbots. However, while open-source data provide algorithms with a diverse, albeit generic resource for learning language, it’s no substitute for use-case-specific training data. 

Accurate, effective chatbots tend to use specific training data. KLM used 60,000 genuine customer questions to train its BlueBot chatbot, and the Rose chatbot at the Las Vegas Cosmopolitan Hotel was built using information gathered from a 2-week consultation with customers and hotel workers. 

If a business or organization already has chat logs or other unstructured written communications, then that’s ideal for creating tailor-made NLP datasets. If not, it’s possible to engineer specific text data and combine that with open-source data if necessary. 

In any case, it’s necessary to label data for NLP if you wish for the eventual model to perform well in specific tasks. 

NLP labeling requires data labeling and linguistic skills. We’ve covered some of the main concepts in our Ultimate Guide. Some texts require syntactic and semantic analysis, involving parsing and preprocessing text and grammar, POS tagging, entity extraction, and relationship linking. 

Labeling intents and sentiments provide NLP algorithms with an understanding of how users angle their queries to the use case.