Question Answer

A Question Answer is a statement that satisfies the informational need of a question.

• Context:
  • It can be measured by a Question Answer Correctness Measure.
  • It can be measured by a Question Answer Correctness Measure.
  • It can be derived from various Knowledge Sources, including databases, documents, or expert opinions.
  • It can be evaluated for quality based on criteria such as accuracy, relevance, and clarity.
  • It can range from being a Single-Word Answer to a Detailed Explanation Answer.
  • It can range from being a Factual Answer to an Opinion-Based Answer.
  • It can range from being a Simple Question Answer to being a Complex Question Answer.
  • It can range from being a Short Question Answer to being a Long Question Answer.
  • It can range from being a Open-Domain Question to being a Domain-Specific Question.
  • It can range from being an Extractive Answer (extracted directly from a text) to being a Generative Answer (generated by information synthesis).
  • It can range from being a Succinct Answer to being a Long-Winded Answer.
  • It can be a List Answer, such as "New York City, Los Angeles, and Chicago" in response to "What are the three largest cities in the United States?"
  • It can be a Table Answer, such as a table displaying the GDP and population of countries.
  • It can be a Yes/No Answer, such as "Yes" to "Is Paris in France?"
  • It can be an Extractive Answer, where the response is pulled directly from a source document.
  • It can be a Generative Answer, where the response is synthesized by integrating multiple pieces of information.
  • It can be an answer to a Structured Query, such as a database query returning specific data.
  • It can be an Unstructured Answer, often seen in more conversational contexts.
  • It can range from being an Under-Specified Answer, which might need further clarification, to a Well-Specified Answer, which is clear and precise, to an Over-Specified Answer, which may provide more detail than required.
  • It can be from a QA Task.
  • It can be a member of a FAQ.
  • It can be a Multiple-Choice Answer.
  • It can be a Definition Answer, such as "A mammal is a vertebrate animal that has hair or fur and females produce milk for their young."
  • It can be a State-of-the-World Answer, such as “The company has filed for bankruptcy.”
  • ...
• Example(s):
  • By Detail Level:
    • Short Answer (e.g., to a simple factual question): “Paris” in response to “What is the capital of France?"
    • Long Answer (e.g., to a detailed question): “Paris, the capital of France, is known for its rich history including its role in the French Revolution, as well as being a major center for art, fashion, and culture."
  • By Type:
    • Factual Answer (e.g., to a factual query): “The capital of France is Paris."
    • Opinion-Based Answer (e.g., to a subjective question): “The best city in France is Paris because of its vibrant culture and historical importance."
    • List Answer (e.g., to a list query): “Paris, Marseille, and Lyon” in response to “Name three large cities in France."
    • Domain-Specific Answer (e.g., to a domain-specific question): “The legal age for drinking in France is 18” addressing a query about French laws.
    • Sentence Answer (e.g., to a straightforward question): “The Eiffel Tower is located in Paris."
  • By Specificity:
    • Yes/No Answer (e.g., to a yes-no question): “Yes” to the question “Is Paris in France?"
    • Table Answer (e.g., to a data-driven question): A table showing GDP and population of major French cities presented in response to “What are the GDP and population figures for major French cities?"
  • ...
• Counter-Example(s):
  • Machine Translation Output such as translating "Bonjour" to "Hello".
  • Natural Language Inference Output such as deducing that "Since John is from Paris, he speaks French."
  • Summarization Output such as summarizing a document about French cities without directly answering a specific question.
• 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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