Closed Domain Question Answering: Leveraging Machine Learning for Focused Knowledge Retrieval
Closed Domain Question Answering (CDQA) systems are designed to answer questions within a specific domain, using machine learning techniques to understand and extract relevant information from a given context. These systems have gained popularity in recent years due to their ability to provide accurate and focused answers, making them particularly useful in educational and professional settings.
CDQA systems can be broadly categorized into two types: open domain models, which answer generic questions using large-scale knowledge bases and web-corpus retrieval, and closed domain models, which address focused questioning areas using complex deep learning models. Both types of models rely on textual comprehension methods, but closed domain models are more suited for educational purposes due to their ability to capture the pedagogical meaning of textual content.
Recent research in CDQA has explored various techniques to improve the performance of these systems. For instance, Reinforced Ranker-Reader (R³) is an open-domain QA system that uses reinforcement learning to jointly train a Ranker component, which ranks retrieved passages, and an answer-generation Reader model. Another approach, EDUQA, proposes an on-the-fly conceptual network model that incorporates educational semantics to improve answer generation for classroom learning.
In the realm of Conversational Question Answering (CoQA), researchers have developed methods to mitigate compounding errors that occur when using previously predicted answers at test time. One such method is a sampling strategy that dynamically selects between target answers and model predictions during training, closely simulating the test-time situation.
Practical applications of CDQA systems include interactive conversational agents for classroom learning, customer support chatbots in specific industries, and domain-specific knowledge retrieval tools for professionals. A company case study could involve an organization using a CDQA system to assist employees in quickly finding relevant information from internal documents, improving productivity and decision-making.
In conclusion, Closed Domain Question Answering systems have the potential to revolutionize the way we access and retrieve domain-specific knowledge. By leveraging machine learning techniques and focusing on the nuances and complexities of specific domains, these systems can provide accurate and contextually relevant answers, making them invaluable tools in various professional and educational settings.
Closed Domain Question Answering
Closed Domain Question Answering Further Reading
1.Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets http://arxiv.org/abs/2008.02637v1 Patrick Lewis, Pontus Stenetorp, Sebastian Riedel2.Do not let the history haunt you -- Mitigating Compounding Errors in Conversational Question Answering http://arxiv.org/abs/2005.05754v1 Angrosh Mandya, James O'Neill, Danushka Bollegala, Frans Coenen3.EDUQA: Educational Domain Question Answering System using Conceptual Network Mapping http://arxiv.org/abs/1911.05013v1 Abhishek Agarwal, Nikhil Sachdeva, Raj Kamal Yadav, Vishaal Udandarao, Vrinda Mittal, Anubha Gupta, Abhinav Mathur4.R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering http://arxiv.org/abs/1709.00023v2 Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerald Tesauro, Bowen Zhou, Jing Jiang5.Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions http://arxiv.org/abs/2210.12979v1 Seonjeong Hwang, Yunsu Kim, Gary Geunbae Lee6.Design and Development of Rule-based open-domain Question-Answering System on SQuAD v2.0 Dataset http://arxiv.org/abs/2204.09659v1 Pragya Katyayan, Nisheeth Joshi7.Context Generation Improves Open Domain Question Answering http://arxiv.org/abs/2210.06349v2 Dan Su, Mostofa Patwary, Shrimai Prabhumoye, Peng Xu, Ryan Prenger, Mohammad Shoeybi, Pascale Fung, Anima Anandkumar, Bryan Catanzaro8.EQuANt (Enhanced Question Answer Network) http://arxiv.org/abs/1907.00708v2 François-Xavier Aubet, Dominic Danks, Yuchen Zhu9.Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks http://arxiv.org/abs/1911.02655v1 Timothy J. Hazen, Shehzaad Dhuliawala, Daniel Boies10.Subjective Question Answering: Deciphering the inner workings of Transformers in the realm of subjectivity http://arxiv.org/abs/2006.08342v2 Lukas MuttenthalerClosed Domain Question Answering Frequently Asked Questions
What is Closed Domain Question Answering (CDQA)?
Closed Domain Question Answering (CDQA) systems are designed to answer questions within a specific domain, using machine learning techniques to understand and extract relevant information from a given context. These systems are particularly useful in educational and professional settings, as they provide accurate and focused answers based on the domain-specific knowledge.
How do CDQA systems differ from Open Domain Question Answering systems?
While both CDQA and Open Domain Question Answering systems rely on textual comprehension methods, the main difference lies in their scope. Open domain models answer generic questions using large-scale knowledge bases and web-corpus retrieval, whereas closed domain models address focused questioning areas using complex deep learning models. Closed domain models are more suited for educational purposes due to their ability to capture the pedagogical meaning of textual content.
What are some recent research advancements in CDQA?
Recent research in CDQA has explored various techniques to improve the performance of these systems. For example, Reinforced Ranker-Reader (R³) is an open-domain QA system that uses reinforcement learning to jointly train a Ranker component, which ranks retrieved passages, and an answer-generation Reader model. Another approach, EDUQA, proposes an on-the-fly conceptual network model that incorporates educational semantics to improve answer generation for classroom learning.
How do CDQA systems handle Conversational Question Answering (CoQA)?
In the realm of Conversational Question Answering (CoQA), researchers have developed methods to mitigate compounding errors that occur when using previously predicted answers at test time. One such method is a sampling strategy that dynamically selects between target answers and model predictions during training, closely simulating the test-time situation.
What are some practical applications of CDQA systems?
Practical applications of CDQA systems include interactive conversational agents for classroom learning, customer support chatbots in specific industries, and domain-specific knowledge retrieval tools for professionals. A company case study could involve an organization using a CDQA system to assist employees in quickly finding relevant information from internal documents, improving productivity and decision-making.
How do CDQA systems leverage machine learning techniques?
CDQA systems use machine learning techniques, such as deep learning models, to understand and extract relevant information from a given context. These models are trained on domain-specific data, allowing them to capture the nuances and complexities of the domain and provide accurate, contextually relevant answers.
What are the challenges and future directions in CDQA research?
Some challenges in CDQA research include improving the performance of these systems, handling compounding errors in conversational question answering, and incorporating educational semantics for better answer generation. Future directions may involve developing more advanced deep learning models, exploring reinforcement learning techniques, and creating more efficient sampling strategies for training and testing.
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