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Daily Human Dialogue Corpus
Data Science and Analytics
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About
This resource provides deep insight into conversation dynamics through multi-turn dialogue experiences, covering a variety of topics reflective of our daily communication way. The data consists of conversations authored by humans, which offers a high degree of authenticity and less noise compared to alternative datasets typically found online. The utility of this product is enhanced by the inclusion of manually labeled fields detailing communication intentions and expressed emotions, making it an invaluable asset for advancing research in dialog systems and conversational AI.
Columns
- dialog: The main conversation turns, provided as text strings, showing the exchange between the two parties.
- act: Categorical labels representing the communication intentions of the participants involved within each turn of the dialogue.
- emotion: Categorical labels identifying any emotions that are expressed during the course of a particular dialogue.
Distribution
This dataset is structured into three primary files:
train.csv, validation.csv, and test.csv. These files enable users to evaluate existing dialogue system approaches or conduct new experiments on conversational models. The data fields provided across all three files are dialog, act, and emotion. The files, such as test.csv, typically contain 1000 valid records and are commonly distributed in CSV format.Usage
This data product is perfectly suited for several high-impact applications:
- Developing next-generation conversational AI systems capable of replicating genuine human conversations by accurately modelling communication intentions and emotion.
- Creating sophisticated interactive chatbots with tailored responses and emotional context, offering users a unique way to understand and improve their conversational skills.
- Building specialised language-learning tools that can generate personalised dialogues to assist foreign language learners in practicing spoken communication.
- Conducting detailed text analysis using statistical methods or Natural Language Processing (NLP) libraries, such as NLTK or Spacy.
- Exploring machine learning tasks, including the generation of novel conversations (e.g., chat bots) using reinforcement learning models or deep learning architectures for natural language understanding.
Coverage
The focus of this collection is on dialogues that accurately mirror human day-to-day communication patterns and experiences. The content is derived from conversations written by humans, ensuring relevance to general, real-life communication scenarios. Specific geographic or time range restrictions are not provided, as the scope relates to universally applicable daily conversations.
License
CC0 1.0 Universal (CC0 1.0) - Public Domain
Who Can Use It
This data product is ideal for a variety of users:
- Researchers seeking novel approaches in dialogue systems and wishing to explore underlying patterns embedded within real-life conversations.
- Students and academics exploring conversation dynamics from a computer science perspective.
- Developers focused on AI and Machine Learning (ML) tasks, particularly those involving natural language understanding and generation.
Dataset Name Suggestions
- Daily Human Dialogue Corpus
- Emotion and Intention Labelled Conversations
- Multi-Turn Communication Data
- Conversational Potential Unlocker
Attributes
Original Data Source: Daily Human Dialogue Corpus
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