Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.

This dataset include JSON file made for University chatbot so it contain information about University Inquiry for ordinary puprose. In this file contains list of intents with tags, pattern, reponses and context set. The file include 38 intents or called tags.This dataset can be used for training and evaluating chatbot models.

To add tags you have to write one important word which included in your every questions or pattern asked by user so that by tag chatbot gives you appropriate answers. For instance, If you want to add questions about fees then your tag name must be fees and for how many hour your collage opens or time of your university then your tag name should be hours. However, this file contains many tags like greetings, fees, numbers, hours, events, floors, canteens, hod, admission and many more. The patterns refers to the questions which you want to include and which you think that user might be ask during their inquiry. The response category filled up by you your response which you want to give to user if they ask any queries. Last, The context_set field is left empty in this case, but it could be used to specify a particular context in which a given intent should be used.

Tis data is collected or edited in october 2022 by manually adding questions and responses.

Usages There are just a few examples of the many ways that chatbots can be used:

1. Education: Chatbots can be used in education to provide students with personalized learning experiences, answer questions about coursework, and provide feedback on assignments.
2. Customer Service: Chatbots can be used to provide customer service support 24/7. They can answer frequently asked questions and provide personalized assistance to customers.
3. Healthcare: Chatbots can be used to provide medical advice, schedule appointments, and help patients manage their health.
4. Banking: Chatbots can be used in the banking industry to help customers with their accounts, answer questions about transactions, and provide information about bank products.
5. Travel: Chatbots can be used in the travel industry to help customers with booking flights, hotels, and rental cars, as well as answer questions about travel destinations.
6. Human Resources: Chatbots can be used in human resources to help employees with their benefits, answer questions about company policies, and provide information about job openings.
7. E-commerce: Chatbots can help customers with product recommendations, track orders, and process payments. They can also provide product information and answer questions.

As technology continues to advance, the potential applications for chatbots will continue to expand.

Snapshot img

Chatbot Market Size 2025-2029

The chatbot market size is forecast to increase by USD 9.63 billion, at a CAGR of 42.9% between 2024 and 2029. Several benefits associated with using chatbots solutions will drive the chatbot market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 37% growth during the forecast period.
By End-user - Retail segment was valued at USD 210.60 billion in 2023
By Product - Solutions segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 billion
Market Future Opportunities: USD 9.63 billion 
CAGR : 42.9%
APAC: Largest market in 2023

Market Summary

The market is a dynamic and evolving landscape, characterized by the integration of advanced technologies and innovative applications. Core technologies such as natural language processing (NLP) and machine learning (ML) enable chatbots to understand and respond to user queries in a conversational manner, transforming customer engagement across industries. However, the lack of standardization and awareness surrounding chatbot services poses a challenge to market growth. As of now, chatbots are increasingly being adopted in various sectors, including healthcare, finance, and e-commerce, with customer service being the primary application. According to recent estimates, over 50% of businesses are expected to invest in chatbots by 2025.
In terms of service types, chatbots can be categorized into rule-based and AI-powered, each offering unique benefits and challenges. Key companies, such as Microsoft, IBM, and Google, are continuously pushing the boundaries of chatbot technology, introducing new features and capabilities. Regulatory frameworks, including GDPR and HIPAA, play a crucial role in shaping the market landscape. Looking ahead, the forecast period presents significant opportunities for growth, as chatbots continue to reshape the way businesses interact with their customers. Related markets such as voice assistants and conversational AI also contribute to the broader context of the market.
Stay tuned for more insights and analysis on this continuously unfolding market.

What will be the Size of the Chatbot Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Chatbot Market Segmented and what are the key trends of market segmentation?

The chatbot industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Retail
  BFSI
  Government
  Travel and hospitality
  Others


Product

  Solutions
  Services


Deployment

  Cloud-Based
  On-Premise
  Hybrid


Application

  Customer Service
  Sales and Marketing
  Healthcare Support
  E-Commerce Assistance


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By End-user Insights

The retail segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth, with adoption in various sectors escalating at a remarkable pace. According to recent reports, the chatbot industry is projected to expand by 25% in the upcoming year, while current market penetration hovers around 27%. This growth can be attributed to the increasing adoption of conversational AI platforms in customer service and e-commerce applications. Unsupervised learning techniques and machine learning models play a pivotal role in chatbot development, enabling natural language processing and understanding. Dialog management systems, including F1-score calculation and dialogue state tracking, ensure effective conversation flow. Human-in-the-loop training and contextual understanding further enhance chatbot performance.

Natural language generation, intent recognition technology, and knowledge graph integration are essential components of advanced chatbot systems. Multi-lingual chatbot support and speech-to-text conversion cater to a diverse user base. Reinforcement learning methods and deep learning algorithms enable chatbots to learn and improve from user interactions. Chatbot development platforms employ various data augmentation methods and active learning strategies to create training datasets for transfer learning applications. Question answering systems and voice-enabled chatbot features provide seamless user experiences. Sentiment analysis techniques and user interface design contribute to enhancing customer engagement and satisfaction. Conversational flow design and response generation models ensure e

About Dataset This dataset is used for research or training of natural language processing (NLP) models. The dataset may include various types of conversations such as casual or formal discussions, interviews, customer service interactions, or social media conversations.

Application - Chatbots and virtual assistants: Conversation datasets are used to train chatbots and virtual assistants to interact with users in a more human-like manner.

• Customer service: Conversation datasets can be used to train customer service chatbots, allowing companies to provide 24/7 customer support without human intervention.

The chatbot market is experiencing robust growth, driven by the increasing adoption of AI-powered solutions across diverse industries. The market's expansion is fueled by several key factors, including the rising demand for enhanced customer experience, the need for 24/7 availability and immediate support, and the potential for significant cost savings through automation. Businesses are leveraging chatbots to streamline operations, personalize customer interactions, and improve efficiency in various functions like customer service, lead generation, and internal communication. This trend is further amplified by advancements in natural language processing (NLP) and machine learning (ML), enabling more sophisticated and human-like chatbot interactions. While challenges remain, such as ensuring data security and maintaining a high level of accuracy, the overall market trajectory remains positive. We estimate the market size to be approximately $5 billion in 2025, growing at a compound annual growth rate (CAGR) of 25% between 2025 and 2033. This growth will be primarily driven by the increasing adoption of chatbots in emerging markets and advancements in AI technologies. The competitive landscape is dynamic, with both established tech giants like IBM Watson and Nuance Communications and agile startups like Artificial Solutions and Creative Virtual vying for market share. The market is segmented by deployment type (cloud-based, on-premise), application (customer service, marketing, healthcare), and industry. While North America currently holds a significant market share, regions like Asia-Pacific are demonstrating rapid growth due to increasing digitalization and a burgeoning e-commerce sector. Restraints on growth include the need for ongoing maintenance and updates, the potential for integration challenges with existing systems, and the ethical considerations associated with AI-powered interactions. However, the long-term outlook for the chatbot market remains exceptionally promising, with continuous innovation and expanding applications likely to further fuel its expansion in the coming years.

This dataset provides detailed records of telecom customer support tickets, including issue types, resolution timelines, agent actions, and customer satisfaction ratings. It enables process optimization, root cause analysis, and AI/ML chatbot training by offering granular insights into ticket lifecycles and outcomes.

Mental Health Chatbot Pairs

AI-based Tailored Support for Mental Health Conversation

By Huggingface Hub [source]

About this dataset

This dataset contains a compilation of carefully-crafted Q&A pairs which are designed to provide AI-based tailored support for mental health. These carefully chosen questions and answers offer an avenue for those looking for help to gain the assistance they need. With these pre-processed conversations, Artificial Intelligence (AI) solutions can be developed and deployed to better understand and respond appropriately to individual needs based on their input. This comprehensive dataset is crafted by experts in the mental health field, providing insightful content that will further research in this growing area. These data points will be invaluable for developing the next generation of personalized AI-based mental health chatbots capable of truly understanding what people need

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains pre-processed Q&A pairs for AI-based tailored support for mental health. As such, it represents an excellent starting point in building a conversational model which can handle conversations about mental health issues. Here are some tips on how to use this dataset to its fullest potential:

• Understand your data: Spend time getting to know the text of the conversation between the user and the chatbot and familiarize yourself with what type of questions and answers are included in this specific dataset. This will help you better formulate queries for your own conversational model or develop new ones you can add yourself.

• Refine your language processing models: By studying the patterns in syntax, grammar, tone, voice, etc., within this conversational data set you can hone your natural language processing capabilities - such as keyword extractions or entity extraction – prior to implementing them into a larger bot system .

• Test assumptions: Have an idea of what you think may work best with a particular audience or context? See if these assumptions pan out by applying different variations of text to this dataset to see if it works before rolling out changes across other channels or programs that utilize AI/chatbot services

• Research & Analyze Results : After testing out different scenarios on real-world users by using various forms of q&a within this chatbot pair data set , analyze & record any relevant results pertaining towards understanding user behavior better through further analysis after being exposed to tailored texted conversations about Mental Health topics both passively & actively . The more information you collect here , leads us closer towards creating effective AI powered conversations that bring our desired outcomes from our customer base .

Research Ideas

• Developing a chatbot for personalized mental health advice and guidance tailored to individuals' unique needs, experiences, and struggles.
• Creating an AI-driven diagnostic system that can interpret mental health conversations and provide targeted recommendations for interventions or treatments based on clinical expertise.
• Designing an AI-powered recommendation engine to suggest relevant content such as articles, videos, or podcasts based on users’ questions or topics of discussion during their conversation with the chatbot

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------| | text | The text of the conversation between the user and the chatbot. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

Airport Digital Twin Chatbot Training Market Outlook

According to our latest research, the global Airport Digital Twin Chatbot Training market size in 2024 stands at USD 1.13 billion, reflecting the rapid adoption of advanced digital solutions in the aviation sector. The market is expected to witness a robust growth trajectory, registering a CAGR of 18.7% from 2025 to 2033. By 2033, the market is projected to reach USD 5.86 billion, driven by increasing investments in airport modernization, the proliferation of artificial intelligence (AI) technologies, and the pressing need for enhanced passenger experience and operational efficiency.

The key growth factor propelling the Airport Digital Twin Chatbot Training market is the escalating demand for real-time data-driven decision-making in airport operations. As airports grapple with growing passenger volumes and heightened security requirements, the integration of digital twin technology with AI-powered chatbots enables seamless simulation, monitoring, and management of complex airport environments. This convergence empowers stakeholders to predict potential bottlenecks, optimize resource allocation, and proactively address operational disruptions. Furthermore, the ability of digital twin chatbots to learn and adapt through continuous training ensures that airports remain agile and responsive to evolving operational challenges, thereby fostering a culture of innovation and continuous improvement.

Another significant driver is the imperative to elevate the passenger experience amid intensifying competition among airports globally. Digital twin chatbots, trained on vast datasets encompassing passenger behavior, flight schedules, and facility management, can deliver personalized assistance, streamline check-in processes, and provide real-time updates, thereby reducing wait times and enhancing overall satisfaction. The adoption of these technologies not only improves passenger engagement but also contributes to brand differentiation for airports and airlines. As customer expectations for seamless, contactless, and efficient services continue to rise, the deployment of intelligent chatbot solutions is becoming a strategic priority for airport operators aiming to secure a competitive edge.

The market’s expansion is further fueled by regulatory mandates and industry initiatives aimed at strengthening airport security and sustainability. Digital twin chatbots play a pivotal role in simulating security scenarios, monitoring compliance, and facilitating rapid response to incidents. Additionally, they support predictive maintenance and energy management, aligning with global efforts to reduce the carbon footprint of aviation infrastructure. The synergy between regulatory compliance, operational resilience, and environmental stewardship is accelerating the adoption of digital twin chatbot training solutions across airports of varying scales and complexities.

From a regional perspective, North America currently leads the market, underpinned by substantial investments in airport infrastructure, a mature digital ecosystem, and the presence of leading technology providers. However, Asia Pacific is poised for the fastest growth, driven by the surge in air travel, large-scale airport development projects, and government initiatives promoting smart airport technologies. Europe remains a significant contributor, with a focus on sustainability and passenger-centric innovations. Meanwhile, the Middle East & Africa and Latin America are emerging as promising markets, supported by strategic investments in aviation and digital transformation efforts.

Component Analysis

The Component segment of the Airport Digital Twin Chatbot Training market is bifurcated into Software and Services. The software sub-segment encompasses the core digital twin platforms, AI-powered chatbot engines, and integrated analytics tools that form the backbone of intelligent airport operations. These solutions are des

Chatbots in Healthcare Market Outlook

The global Chatbots in Healthcare market size was valued at approximately USD 123.2 million in 2023 and is projected to reach USD 1,274.6 million by 2032, growing at a remarkable CAGR of 29.8% during the forecast period. The rapid growth of this market is driven by technological advancements and increasing demand for efficient and cost-effective healthcare solutions.

The growth of the chatbots in healthcare market can be attributed to several critical factors. Firstly, the rising adoption of artificial intelligence (AI) and machine learning (ML) in healthcare is a significant driver. These technologies enable chatbots to provide more accurate and personalized responses, improving patient engagement and satisfaction. Secondly, the increasing prevalence of chronic diseases and the growing aging population are driving the need for efficient healthcare management solutions. Chatbots can offer 24/7 support, helping to manage patient inquiries and reducing the burden on healthcare professionals.

Furthermore, the COVID-19 pandemic has accelerated the adoption of digital health technologies, including chatbots. With the surge in telemedicine and remote healthcare services, chatbots have become essential tools for managing patient inquiries, triaging symptoms, and providing information on COVID-19. Additionally, the cost-effectiveness of chatbots compared to traditional customer service and healthcare support methods is another crucial growth factor. By automating routine tasks, healthcare providers can allocate resources more efficiently and reduce operational costs.

On the regional front, North America holds the largest share of the chatbots in healthcare market due to advanced healthcare infrastructure, high adoption of digital health solutions, and significant investments in AI technologies. Europe follows closely behind, with countries like the UK, Germany, and France leading the way. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by increasing healthcare expenditure, rapid digitalization, and a growing focus on improving healthcare access and quality. Latin America, the Middle East, and Africa are also experiencing steady growth due to rising awareness and adoption of digital health technologies.

Component Analysis

The chatbots in healthcare market can be segmented by component into software and services. The software segment is expected to dominate the market owing to the increasing development of advanced chatbot solutions that leverage AI and ML for more accurate and efficient interactions. These software solutions are designed to handle a wide range of tasks, from answering basic health-related questions to providing personalized medical advice based on patient data. The growing demand for integrated healthcare solutions that can seamlessly interact with electronic health records (EHRs) and other healthcare systems further propels the growth of the software segment.

On the other hand, the services segment, which includes implementation, maintenance, and training services, is also witnessing significant growth. As healthcare providers increasingly adopt chatbot solutions, there is a rising need for services that ensure smooth integration and optimal performance. Implementation services help organizations customize chatbot solutions to meet their specific needs, while maintenance services ensure that chatbots remain up-to-date and function correctly. Additionally, training services are crucial for educating healthcare staff on effectively using and managing chatbot solutions, further enhancing their utility and adoption.

The synergy between software and services is a critical aspect of the chatbots in healthcare market. While software provides the core functionality, services ensure that these solutions are effectively implemented and maintained, leading to better patient outcomes and improved operational efficiency. As the market continues to evolve, we can expect further advancements in both software capabilities and the range of services offered, contributing to the overall growth of the market.

Report Scope

File Description

This dataset contains Twitter support conversations collected from various company accounts. It includes customer inquiries and corresponding support responses. The data is useful for training AI chatbots, analyzing customer service trends, and developing sentiment analysis models.

Column Description

This dataset contains customer support interactions on Twitter. It includes the following columns: tweet_id: A unique identifier for each tweet. author_id: The unique ID of the user who posted the tweet. inbound: A boolean value indicating whether the tweet is from a customer (True) or from the support team (False). created_at: The timestamp of when the tweet was posted (in UTC format). text: The content of the tweet. response_tweet_id: The unique ID of the response tweet, if applicable. in_response_to_tweet_id: The ID of the original tweet to which this tweet is responding.

How This Data Can Be Used? Training a chatbot: Helps in generating automated support responses. Sentiment analysis: Can analyze whether tweets are complaints, queries, or feedback. Conversation tracking: By linking response tweets with original messages.

originalAuthor : MANORAMA Source : https://www.kaggle.com/datasets/manovirat/aspect/data

Note: This dataset is shared for educational and research purposes only.

In 2023, roughly ** percent of people in Germany said they would find a customer service chatbot useful for flights and hotels, as well as utility services. ** percent of people were not interested in the help of a chatbot. The rise of chatbots ChatGPT was launched in November 2022, and although chatbots existed prior, it was the first one that allowed users to dictate the length, and style of, as well as direct a conversation. Since this AI technology is so versatile, there are many different purposes for which it can be used. For example, some people use the software to help them understand complex theories they are learning for their studies, whilst others ask the chatbot to plan their meals for the week. Almost ** percent of ChatGPT users were aged 18 to 34 in 2023, whilst only **** percent were over the age of 55. When it comes to creating chatbots companies are facing challenges since the technology is new and highly complex. For most companies, the biggest difficulty is data management. This is due to the fact that so much data is required to train AI programs and when they are used, there is also a huge amount of data generated. Commercial usage of chatbots One industry that has been using chatbots for the past couple of years is the online shopping industry. The most popular function of chatbots among online shoppers globally was searching for product information. This was also the top result for consumers in Germany, followed by customer service and sending of updates about products. However, Germany did have a **************** of chatbots than the global average. Similarly, when it came to the share of those shopping online who considered chatbot customer service useful, Germany also ranked quite low, with only ** percent of respondents stating that they found it useful. Other countries such as India, UAE, and Indonesia had a *********** uptake rate.

The Conversational AI Software market is experiencing robust growth, driven by the increasing adoption of AI-powered chatbots and virtual assistants across various industries. The market, valued at approximately $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This significant expansion is fueled by several key factors. Businesses are increasingly leveraging conversational AI to enhance customer service, automate tasks, and improve operational efficiency. The rising demand for personalized customer experiences and the need for 24/7 availability are further bolstering market growth. Advancements in Natural Language Processing (NLP) and Machine Learning (ML) technologies are enabling more sophisticated and human-like interactions, leading to wider adoption. The market is segmented based on deployment (cloud, on-premises), application (customer service, sales, marketing), and industry (BFSI, healthcare, retail). Key players in the Conversational AI Software market include established tech giants like SAP, IBM, and Microsoft, alongside specialized AI solution providers such as Ada, Kore.ai, Conversica, LivePerson, Genesys, Boost.ai, and many others. These companies are actively investing in research and development, strategic partnerships, and acquisitions to enhance their product offerings and expand their market reach. Despite the positive growth trajectory, challenges remain, including concerns over data security and privacy, the need for high-quality training data, and the complexity of integrating conversational AI solutions into existing business systems. However, the overall market outlook remains optimistic, with continued innovation and expanding applications expected to drive sustained growth in the coming years.

The Bot Platforms Software market, currently valued at $956 million in 2025, is projected to experience robust growth, driven by the increasing adoption of AI-powered chatbots across diverse industries. This growth is fueled by the need for enhanced customer service, automation of routine tasks, and the rising demand for personalized user experiences. Key market drivers include the decreasing cost of cloud computing resources, advancements in natural language processing (NLP) and machine learning (ML) technologies, and the growing integration of bots across various platforms like messaging apps, websites, and social media. The market is segmented by deployment (cloud, on-premise), application (customer service, marketing, sales), and organization size (small, medium, large). Leading players like Amazon, Google, Microsoft, and IBM are actively shaping the market landscape through continuous innovation and strategic partnerships, while smaller, specialized players focus on niche applications. The competitive landscape is dynamic, with mergers and acquisitions expected to further consolidate the market. The forecasted Compound Annual Growth Rate (CAGR) of 10.4% from 2025 to 2033 signifies a considerable expansion in market size. This consistent growth trajectory reflects the ongoing digital transformation across sectors and the increasing reliance on automation to optimize processes and improve operational efficiency. The market faces challenges such as data security concerns, integration complexities, and the need for robust training data to ensure accurate chatbot performance. However, these challenges are likely to be mitigated through technological advancements and the development of more sophisticated and secure bot platform solutions. The market's future is promising, with significant opportunities for growth in emerging markets and expansion into new application areas, solidifying bot platforms as an essential component of the modern digital ecosystem.

The proposed research intends to improve the current service desk model by using Conversational Language Understanding (CLU) processes embedded in the chatbot model, to understand the user’s input and automate the ticket resolution process as well as improve the customer service experience and efficiency. The CLU data will be trained, thus it will be able to cover all the possible user input. The chatbot will then be designed to have five main dialogue flows consisting of, changing the user’s current password, checking the user’s mobile number that is listed in Azure Active Directory (AAD), updating the user’s mobile number in AAD, creating a new ticket to the ticketing system, and creating a follow-up ticket to the ticketing system. A trained CLU data with a high prediction score based on the proposed dialogue flow will then be embedded with the chatbot design. It would produce a next-level chatbot that is able to understand the user’s intent, classify the user’s intent, automate the user’s Level 1 (L1) proposed request without any human technician’s interaction, and create a ticket in the ticketing system for any request that is not covered by the chatbot yet.

The AI for Customer Service market is experiencing robust growth, driven by the increasing demand for efficient, cost-effective, and personalized customer support solutions. Businesses across various sectors are adopting AI-powered tools like chatbots, virtual assistants, and sentiment analysis systems to enhance customer experience and streamline operations. The market's expansion is fueled by several factors, including the rising adoption of cloud-based solutions offering scalability and flexibility, the increasing availability of large datasets for training AI models, and the growing need for 24/7 customer support. The market is segmented by deployment type (on-premise and cloud-based) and application (e-commerce, enterprise sales, and others). Cloud-based solutions dominate the market due to their inherent advantages in accessibility, cost-effectiveness, and ease of integration. While the on-premise segment caters to specific security and compliance requirements of certain industries. The e-commerce sector is currently the largest application segment, but the enterprise sales segment is expected to witness significant growth in the coming years. We project the market to be valued at $15 billion in 2025, expanding at a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This growth trajectory reflects the ongoing digital transformation across industries and the increasing reliance on AI to optimize customer interactions. This market growth is anticipated to be influenced by several factors. The increasing sophistication of AI algorithms, leading to more natural and human-like interactions, is a key driver. Furthermore, advancements in Natural Language Processing (NLP) and Machine Learning (ML) are enabling AI systems to understand and respond to customer queries with greater accuracy and efficiency. However, challenges remain, including concerns about data privacy, security, and the need for continuous model training and improvement. The market's competitive landscape is highly fragmented, with numerous established players and emerging startups vying for market share. Key players are focusing on developing innovative solutions, strategic partnerships, and expanding their global footprint to maintain their competitive edge. The geographic distribution of the market reveals North America and Europe as the leading regions, with significant growth potential in Asia-Pacific.

Bitext Sample Pre-built Customer Service Evaluation Dataset for English

Overview

This Evaluation dataset contains example utterances taken from the "change order" intent from Bitext's pre-built Customer Service domain (which itself covers common intents present across Bitext's 20 pre-built domains). The data can be used to evaluate intent recognition models Natural Language Understanding (NLU) platforms.

Utterances

The dataset contains 10,000 utterances, extracted from a larger dataset of over 1,000,000 utterances, including language register variations such as politeness, colloquial, swearing, indirect style... To select the utterances, we use stratified sampling to generate a dataset with a general user language register profile.

The dataset also reflects commonly occurring linguistic phenomena of real-life chatbots, such as: - spelling mistakes - run-on words - missing punctuation

Contents

Each entry in the dataset contains an example utterance along with its corresponding intent, category and additional linguistic information. Each line contains the following four fields: - flags: the applicable linguistic flags - utterance: an example user utterance - category: the high-level intent category - intent: the intent corresponding to the user utterance

Linguistic flags

The dataset contains annotations for linguistic phenomena, which can be used to adapt bot training to different user language profiles. These flags are: B - Basic syntactic structure L - Lexical variation (synonyms) M - Morphological variation (plurals, tenses…) C - Complex/Coordinated syntactic structure E - Expanded abbreviations (I'm -> I am, I'd -> I would…) I - Interrogative structure K - Keyword only P - Politeness variation Q - Colloquial variation W - Offensive language Z - Noise (spelling, punctuation…)

These phenomena make the training dataset more effective and make bots more accurate and robust.

Categories and Intents

The intent categories covered by the dataset are: ORDER

The intents covered by the dataset are: change_order

(c) Bitext Innovations, 2022

Built In Auto Chatbot Market Outlook

The global market size for Built In Auto Chatbots is projected to grow from USD 3.2 billion in 2023 to USD 11.9 billion by 2032, at a compound annual growth rate (CAGR) of 15.6% during the forecast period. The increasing integration of AI and machine learning technologies across various sectors is a significant growth factor contributing to this expansion. Factors such as enhancing customer experience, reducing operational costs, and the rising demand for 24/7 customer support are propelling the market forward.

One of the primary growth factors in the built-in auto chatbot market is the advancement in AI technologies. As AI continues to evolve, chatbots are becoming more sophisticated, offering more accurate and human-like interactions. This advancement is crucial for businesses aiming to improve customer satisfaction and retain customers. Furthermore, the integration of natural language processing (NLP) with chatbots allows for better understanding and response to customer queries, significantly enhancing the user experience. Companies are increasingly adopting these advanced chatbots to streamline their operations, reduce response time, and provide personalized services.

Another driving factor is the cost-effectiveness that chatbots offer to businesses. Traditional customer support systems require substantial investments in human resources, training, and infrastructure. In contrast, chatbots provide a scalable solution that can handle multiple queries simultaneously without additional costs. This efficiency is particularly beneficial for small and medium enterprises (SMEs) that may have limited resources but still aim to provide high-quality customer service. The ability of chatbots to operate round the clock without breaks further adds to their appeal, ensuring that customer queries are addressed promptly at any time of day.

Moreover, the growing trend of digital transformation across various industries is significantly contributing to the market growth. As businesses increasingly move towards digital platforms, the need for automated solutions like chatbots is rising. These tools not only aid in managing customer interactions but also in gathering valuable customer data, which can be used to enhance products and services. The adaptability of chatbots to various applications—ranging from customer support and sales to HR and IT helpdesk—makes them indispensable in the modern business landscape.

Regionally, North America is expected to hold the largest market share during the forecast period. This dominance is attributed to the early adoption of advanced technologies and the presence of major market players in the region. However, significant growth is also anticipated in the Asia Pacific region, driven by rapid economic development and increased digitalization efforts. Countries like China, India, and Japan are making substantial investments in AI and machine learning technologies, further bolstering the market for built-in auto chatbots.

Component Analysis

The built-in auto chatbot market can be segmented by components into software, hardware, and services. The software segment is expected to dominate the market throughout the forecast period. This dominance can be attributed to the increasing demand for sophisticated chatbot solutions that leverage AI and machine learning to provide more accurate and human-like interactions. Software solutions are continuously evolving, with new features and capabilities being added regularly, which enhances their appeal to businesses aiming to improve customer service and operational efficiency. Additionally, the rise of cloud-based solutions has made it easier for businesses to deploy and manage chatbot software, further driving the growth of this segment.

The hardware segment, while smaller, plays a crucial role in the overall functionality of built-in auto chatbots. Hardware components such as servers and networking equipment are essential for the smooth operation of chatbot systems, especially for on-premises deployments. As businesses increasingly seek to maintain control over their data and ensure high levels of security, the demand for reliable and robust hardware solutions is expected to grow. Innovations in hardware technology, such as the development of specialized AI chips, are also contributing to the efficiency and performance of chatbot systems, making this segment an integral part of the market.

Services encompass a wide range of offerings, from consulting and implementation to training and maintenance. The services segment is ex

The global Artificial Intelligence (AI) chatbot market is experiencing robust growth, driven by increasing digitalization across industries and the need for enhanced customer engagement and operational efficiency. While precise market figures for the study period (2019-2033) are unavailable, a plausible estimate based on industry reports and the provided information suggests a considerable market size. Assuming a conservative CAGR (Compound Annual Growth Rate) of 25% from a base year of 2025, and a 2025 market value of $10 billion (a reasonable estimate considering current market trends), the market could reach approximately $25 billion by 2033. Key drivers include the rising adoption of cloud-based solutions, advancements in Natural Language Processing (NLP) and Machine Learning (ML), and the growing demand for 24/7 customer support. Emerging trends such as the integration of AI chatbots with other technologies like CRM systems and the rise of conversational AI are further fueling market expansion. However, challenges like data security concerns, the need for robust training data, and the potential for biases in AI algorithms act as restraints. Market segmentation is influenced by deployment (cloud, on-premise), application (customer service, marketing, healthcare), and industry vertical (banking, retail, etc.). Leading players, including IBM, 24/7.ai, Google, and others, are aggressively developing and deploying AI chatbot solutions to capture market share. The competitive landscape is highly dynamic, with established tech giants and emerging startups competing for market dominance. Strategic partnerships, acquisitions, and continuous innovation are key competitive strategies. The future growth of the AI chatbot market hinges on overcoming existing challenges, fostering trust in AI systems, and meeting the evolving demands of businesses and consumers for personalized and seamless conversational experiences. Further development of more sophisticated NLP capabilities, improved contextual understanding, and greater integration with other business processes will shape the market trajectory. The ongoing need for effective customer service, automation of tasks, and data-driven decision-making will ensure that AI chatbots remain a critical component of many businesses' operational infrastructure.

Train conversational AI with the ChatBot Dataset for Transformers. Featuring human-like dialogues, preprocessed inputs, and labels, it’s perfect for GPT, BERT, T5, and NLP projects