User:An anonymous user with secrets/sandbox/Error-driven learning
Error-driven learning is a sub-branch of reinforcement learning, where an agent learns from its own actions' feedback, to adjust its behavior. It is similar to other types of machine learning, but differs, as it relies on feedback from the environment rather than explicit labels or categories.[1] These learning models are based on the idea language acquisition involves the minimization of prediction error(the difference between what was predicted and what was actually observed).[2]
Error-driven learning is the basis for a vast array of computational models in the brain and cognitive sciences. These models iteratively adjust expectations based on prediction error, simplifying the computational complexity of running simulations.[2] These methods have been successfully applied in many areas of natural language processing (NLP), including part-of-speech tagging, parsing, word sense disambiguation (WSD), named entity recognition (NER), machine translation (MT), speech recognition (SR) and and dialogue systems.
Definition
Algorithms
The most common error-driven learning algorithm is the GeneRec algorithm, which is used for gene prediction in DNA sequences . Other Error-driven learning algorithms include the backpropagation algorithm used in neural networks, and the Q-learning algorithm used in reinforcement learning
Applications in natural language processing
Part-of-speech tagging
This is the task of assigning a word class (such as noun, verb, adjective, etc.) to each word in a sentence. Error-driven learning can help the model learn from its mistakes and improve its accuracy over time.
Parsing
This is the task of analyzing the syntactic structure of a sentence and producing a tree representation that shows how the words are related. Error-driven learning can help the model learn from its errors and adjust its parameters to produce more accurate parses.
Named entity recognition
This is the task of identifying and classifying entities (such as persons, locations, organizations, etc.) in a text. Error-driven learning can help the model learn from its false positives and false negatives and improve its recall and precision on (NER)
Machine translation
This is the task of translating a text from one language to another. Error-driven learning can help the model learn from its translation errors and improve its quality and fluency.
Speech recognition
This is the task of converting spoken words into written text. Error-driven learning can help the model learn from its recognition errors and improve its accuracy and robustness.
Dialogue systems
These are systems that can interact with humans using natural language, such as chatbots, virtual assistants, or conversational agents. Error-driven learning can help the model learn from its dialogue errors and improve its understanding and generation abilities.
Limitations
References
- ^ Sadre, Ramin; Pras, Aiko (2009-06-19). Scalability of Networks and Services: Third International Conference on Autonomous Infrastructure, Management and Security, AIMS 2009 Enschede, The Netherlands, June 30 - July 2, 2009, Proceedings. Springer. ISBN 978-3-642-02627-0.
- ^ a b Hoppe, Dorothée B.; Hendriks, Petra; Ramscar, Michael; van Rij, Jacolien (2022-10-01). "An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective". Behavior Research Methods. 54 (5): 2221–2251. doi:10.3758/s13428-021-01711-5. ISSN 1554-3528. PMC 9579095. PMID 35032022.
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