Question Answer

A Question Answer is a statement that satisfies the information requirements of a question.

• Context:
  • It can range from being a Succinct Answer to being a Long-Winded Answer.
  • It can measured by a Question Answer Correctness Measure.
  • ...
• Example(s):
  • a Factoid Answer.
  • a List Answer.
  • a Sentence Answer.
  • ...
• Cointer-Example(s):
  • a Machine Translation Output.
  • a Natural Language Inference Ouptut.
  • a Summarization Output.
• See: QA Task, Q&A.

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References

2023

• GBard
  • Answers in question answering tasks can be categorized in different ways, depending on the type of question, the context in which the question is asked, and the desired output.
  • One common way to categorize answers is by their format:
    • Extractive answers: These answers are extracted directly from the given context, such as a document, paragraph, or table. For example, if the question is "What is the capital of France?", the extractive answer would be "Paris".
    • Generative answers: These answers are generated by the model, based on its understanding of the context and the question. For example, if the question is "Why is Paris the capital of France?", the generative answer might be something like "Paris is the capital of France because it is the most populous city in the country and has a long and rich history."
  • Another way to categorize answers is by their level of detail:
    • Short answers: These answers are concise and to the point, typically only a few words or a sentence long. For example, the short answer to the question "What is the capital of France?" is "Paris".
    • Long answers: These answers are more detailed and informative, and may include multiple sentences or paragraphs. For example, the long answer to the question "Why is Paris the capital of France?" might include information about the city's history, population, culture, and government.
  • Finally, answers can also be categorized by their type:
    • Factual answers: These answers are based on objective facts and evidence. For example, the factual answer to the question "What is the capital of France?" is "Paris".
    • Opinion-based answers: These answers are based on the model's own opinion or judgment. For example, the opinion-based answer to the question "What is the best city in the world?" might be "New York City".
  • In addition to these general categories, there are also a number of more specific types of answers that can be used in question answering tasks. For example:
    • Yes/no answers: These answers are simply "yes" or "no". For example, the yes/no answer to the question "Is Paris the capital of France?" is "yes".
    • List answers: These answers are a list of items. For example, the list answer to the question "What are the three largest cities in the United States?" might be "New York City, Los Angeles, and Chicago".
    • Table answers: These answers are presented in a table format. For example, the table answer to the question "What are the population and GDP of the top 10 countries in the world?" might be a table with two columns, one for population and one for GDP.
  • ...
  • It is important to note that the distinction between answers and outputs for other NLP tasks is not always clear-cut. For example, a question like "What is the code to print the message 'Hello, world!' to the console?" could be answered either by extracting the code from a given context (extractive QA) or by generating the code from scratch (generative QA).

2015

• http://www.nytimes.com/2015/08/31/science/oliver-sacks-dies-at-82-neurologist-and-author-explored-the-brains-quirks.html
  • In “Uncle Tungsten,” Dr. Sacks described how growing up in a household of polymaths fostered his interest in science. “The thousand and one questions I asked as a child,” he wrote, “were seldom met by impatient or peremptory answers, but careful ones which enthralled me (though they were often above my head). I was encouraged from the start to interrogate, to investigate.”

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