Open Domain Question Answering (ODQA) is a field of study that focuses on developing systems capable of answering questions from a vast range of topics using large collections of documents.
In ODQA, models are designed to retrieve relevant information from a large corpus and generate accurate answers to user queries. This process often involves multiple steps, such as document retrieval, answer extraction, and answer re-ranking. Recent advancements in ODQA have led to the development of dense retrieval models, which capture semantic similarity between questions and documents rather than relying on lexical overlap.
One of the challenges in ODQA is handling questions with multiple answers or those that require evidence from multiple sources. Researchers have proposed various methods to address these issues, such as aggregating evidence from different passages and re-ranking answer candidates based on their relevance and coverage.
Recent studies have also explored the application of ODQA in emergent domains, such as COVID-19, where information is rapidly changing and there is a need for credible, scientific answers. Additionally, researchers have investigated the potential of reusing existing text-based QA systems for visual question answering by rewriting visual questions to be answerable by open domain QA systems.
Practical applications of ODQA include:
1. Customer support: ODQA systems can help answer customer queries by searching through large databases of technical documentation, reducing response times and improving customer satisfaction.
2. Information retrieval: ODQA can be used to efficiently find answers to free-text questions from a large set of documents, aiding researchers and professionals in various fields.
3. Fact-checking and combating misinformation: ODQA systems can help verify information and provide accurate answers to questions, reducing the spread of misinformation in emergent domains.
A company case study is Amazon Web Services (AWS), where researchers proposed a zero-shot open-book QA solution for answering natural language questions from AWS technical documents without domain-specific labeled data. The system achieved a 49% F1 and 39% exact match score, demonstrating the potential of ODQA in real-world applications.
In conclusion, ODQA is a promising field with numerous applications across various domains. By developing models that can handle a broad range of question types and effectively retrieve and aggregate information from multiple sources, ODQA systems can provide accurate and reliable answers to users' queries.
Open Domain Question Answering
Open Domain Question Answering Further Reading
1.QAMPARI: : An Open-domain Question Answering Benchmark for Questions with Many Answers from Multiple Paragraphs http://arxiv.org/abs/2205.12665v2 Samuel Joseph Amouyal, Ohad Rubin, Ori Yoran, Tomer Wolfson, Jonathan Herzig, Jonathan Berant2.Zero-Shot Open-Book Question Answering http://arxiv.org/abs/2111.11520v1 Sia Gholami, Mehdi Noori3.Learning to answer questions http://arxiv.org/abs/1309.1125v1 Ana Cristina Mendes, Luísa Coheur, Sérgio Curto4.Open-Domain Question-Answering for COVID-19 and Other Emergent Domains http://arxiv.org/abs/2110.06962v1 Sharon Levy, Kevin Mo, Wenhan Xiong, William Yang Wang5.Knowledge-Aided Open-Domain Question Answering http://arxiv.org/abs/2006.05244v1 Mantong Zhou, Zhouxing Shi, Minlie Huang, Xiaoyan Zhu6.Can Open Domain Question Answering Systems Answer Visual Knowledge Questions? http://arxiv.org/abs/2202.04306v1 Jiawen Zhang, Abhijit Mishra, Avinesh P. V. S, Siddharth Patwardhan, Sachin Agarwal7.Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets http://arxiv.org/abs/2008.02637v1 Patrick Lewis, Pontus Stenetorp, Sebastian Riedel8.Towards Universal Dense Retrieval for Open-domain Question Answering http://arxiv.org/abs/2109.11085v1 Christopher Sciavolino9.Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering http://arxiv.org/abs/1711.05116v2 Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell10.AmbigQA: Answering Ambiguous Open-domain Questions http://arxiv.org/abs/2004.10645v2 Sewon Min, Julian Michael, Hannaneh Hajishirzi, Luke ZettlemoyerOpen Domain Question Answering Frequently Asked Questions
What is open domain question answering?
Open Domain Question Answering (ODQA) is a research area in artificial intelligence that focuses on developing systems capable of answering questions on a wide range of topics using large collections of documents. These systems retrieve relevant information from a vast corpus and generate accurate answers to user queries, often involving multiple steps such as document retrieval, answer extraction, and answer re-ranking.
What is the difference between question answering and open domain question answering?
Question Answering (QA) is a broader field that encompasses various types of question-answering systems, including both open domain and closed domain systems. Open Domain Question Answering (ODQA) specifically deals with answering questions from a wide range of topics using large collections of documents, whereas Closed Domain Question Answering focuses on answering questions within a specific, limited domain or subject area.
What is closed domain question answering?
Closed Domain Question Answering is a subfield of question answering that focuses on developing systems capable of answering questions within a specific, limited domain or subject area. These systems are designed to work with a narrower set of documents or knowledge sources, making them more specialized and accurate within their domain but less versatile compared to open domain question answering systems.
What is an example of a question answering system?
An example of a question answering system is IBM's Watson, which gained fame by winning the Jeopardy! game show in 2011. Watson is a powerful AI system that can process and understand natural language queries, search through vast amounts of data, and generate accurate answers to questions in real-time.
How do dense retrieval models improve open domain question answering?
Dense retrieval models improve open domain question answering by capturing semantic similarity between questions and documents, rather than relying on lexical overlap. This allows the models to better understand the meaning of the questions and the content of the documents, leading to more accurate and relevant information retrieval and answer generation.
What are some challenges in open domain question answering?
Some challenges in open domain question answering include handling questions with multiple answers, requiring evidence from multiple sources, and dealing with ambiguous or complex queries. Researchers have proposed various methods to address these issues, such as aggregating evidence from different passages, re-ranking answer candidates based on their relevance and coverage, and using advanced natural language understanding techniques.
How can open domain question answering be applied in real-world scenarios?
Open domain question answering can be applied in various real-world scenarios, such as customer support, information retrieval, and fact-checking. ODQA systems can help answer customer queries by searching through large databases of technical documentation, efficiently find answers to free-text questions from a large set of documents for researchers and professionals, and verify information to reduce the spread of misinformation in emergent domains.
What is the role of open domain question answering in combating misinformation?
Open domain question answering systems can play a crucial role in combating misinformation by providing accurate and reliable answers to questions. By effectively retrieving and aggregating information from multiple sources, ODQA systems can help verify information, reduce the spread of misinformation, and promote the dissemination of credible, scientific knowledge in emergent domains.
How does Amazon Web Services (AWS) utilize open domain question answering?
Amazon Web Services (AWS) researchers proposed a zero-shot open-book QA solution for answering natural language questions from AWS technical documents without domain-specific labeled data. The system achieved a 49% F1 and 39% exact match score, demonstrating the potential of open domain question answering in real-world applications such as customer support and technical documentation retrieval.
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