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Alexa Question and Answer
Machine Learning and AI
Tags and Keywords
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Free
About
This dataset contains user queries about a range of general knowledge topics, each paired with Alexa's answer, a relevancy or quality score, and categorised topics. This collection is useful for studying the accuracy and relevance of Alexa's responses and could be leveraged to improve or benchmark question-answering systems.
Dataset Features:
- AX_ID: Unique identifier for Alexa Question and Answer.
- Question: A user-posed question directed towards Alexa, covering diverse topics.
- Answer: Alexa's response to the user's query, providing an informative or factual answer.
- Topics: Categories or themes relevant to the question, such as “film and TV,” “food,” “sports,” etc., enabling topic-specific analysis.
Usage:
This dataset is ideal for training machine learning models in natural language processing, especially in the areas of question-answering and conversational AI. Potential applications include:
- Developing and testing Q&A models that improve response accuracy.
- Analysing user intent and creating models that enhance Alexa’s response relevance.
- Benchmarking question-answering algorithms in a real-world virtual assistant context.
Coverage:
The dataset covers a wide array of queries commonly directed to Alexa, with answers that vary in depth and relevance. This can help in understanding user needs, optimising response quality, and addressing limitations in AI-driven customer support.
License:
CC0 (Public Domain)
Who can use it:
This dataset is suited for data scientists, AI developers, machine learning researchers, and students focusing on conversational AI, user experience optimisation, and language processing for virtual assistants.
How to use it:
- Improve AI response mechanisms by analysing and refining answer quality.
- Conduct a correlation analysis between question types and response quality.
- Develop user intent classification models to enhance AI-driven conversational systems.
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