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Human-AI Dialogue System Training Data
Data Science and Analytics
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About
This collection of data provides valuable records of real-world interactions between humans and advanced AI-driven chatbots. It serves as an essential knowledge base for trainable Natural Language Processing (NLP) models, offering insights into the complex dynamics of human-machine conversations. Researchers and developers can use this material to explore the intricacies of dialogue, including how machines simulate natural conversation behaviour, such as intonation or even humorous and sarcastic insights. Furthermore, the data enables comparative studies between human behaviour and the capabilities of artificial intelligence in maintaining meaningful dialogue. It is an invaluable resource for developing highly advanced AI systems capable of closely imitating discernible speech patterns through sophisticated trained technology models.
Columns
This dataset primarily includes two string columns:
- chat: Contains dialogues and utterances initiated by the human user.
- system: Holds the generated responses produced by the AI-driven chatbot.
Distribution
The data is contained within a file named
train.csv, which has a size of 260.62 MB. It consists of 113k valid records across two columns. The 'chat' column contains approximately 89.2k unique values, while the 'system' column contains around 36.5k unique values.Usage
This data is ideal for training models designed to enhance conversational abilities. It can be used to build an AI system capable of responding intelligently in natural language conversations, enabling the system to further engage users by providing meaningful replies as the dialogue progresses. Applications include:
- AI-driven natural language generation: Training AI systems to automatically produce realistic conversations between humans and machines.
- Automatic response selection: Developing AI algorithms that select the most appropriate reply in any given conversational context.
- Evaluating human-machine interaction: Identifying areas for improvement in existing dialogue systems and assessing various techniques for creating more effective interactions.
The dataset is suitable for training supervised learning models, such as a sequence-to-sequence (seq2seq) network, or unsupervised methods like autoencoders.
Coverage
This dataset focuses on conversational dynamics between humans and AI agents. The sources do not specify explicit geographic locations, specific time ranges, or defined demographic scopes for the individuals participating in the conversation logs. The data reflects a knowledge base for trainable natural language processing.
License
CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
Who Can Use It
This material is primarily intended for:
- Machine Learning Engineers: To train models that require conversational data, such as designing intelligent virtual assistants.
- AI Developers: Seeking to create astounding AI systems that closely mimic natural, comprehensible speech patterns.
- Academic Researchers: Interested in evaluating human-machine interaction, exploring conversational nuances, and conducting comparative studies between human behaviour and AI dialogue capabilities.
Dataset Name Suggestions
- AI Conversational Knowledge Base V2
- Chatbot Dialogue Interaction Data
- Glaive Function Calling Conversational Logs
- Human-AI Dialogue System Training Data
Attributes
Original Data Source: Human-AI Dialogue System Training Data
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