User:An anonymous user with secrets/sandbox/Error-driven learning
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Error-driven learning is a type of reinforcement learning algorithm. This algorithm adjusts the parameters of a model based on the difference between the desired and actual outputs. These models are characterized by relying on feedback from its environment rather than explicit labels or categories.[1] They are based on the idea that language acquisition involves the minimization of the prediction error (MPSE).[2] Through these prediction errors, these models keep adjusting expectations and simplify computational complexity. These algorithms are usually run by the GeneRec algorithm.[3]
Error-driven learning is the basis for a vast array of computational models in the brain and cognitive sciences. [2] These methods have also been successfully applied in many areas of natural language processing (NLP), including part-of-speech tagging[4], parsing[4] named entity recognition (NER)[5], machine translation (MT)[6], speech recognition (SR)[4] and and dialogue systems[7].
Formal Definition
Algorithms
The most common error backpropagation learning algorithm is the GeneRec(generalized recirculation algorithm), which is used for gene prediction in DNA sequences. In fact, all other error-driven learning algorithms use an alternative version of GeneRec.[3]
Significance
Cognitive science
*Many simple error-driven learning models models turn out to be able to explain seemingly complex phenomena of human cognition and sometimes even predict behavior that more optimal and rational models or more complex networks fail to explain.*
NLPs
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.[4]
Parsing
Parsing 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.[4]
*A sentence is made up of multiple phrases and each phrase, in turn, is made of phrases or words. Each phrase has a head word which may have strong syntactic re- lations with other words in the sentence. Consider the phrases, her hard work and the hard surface. The head words work and surface are indicative of the calling for stamina/endurance and not easily penetrable senses of hard.*
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).[5]
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.[6]
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.[4]
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.[7]
Limitations
One criticism of Error-driven learning is that it can lead to overfitting and generalization issues if not implemented properly . Another criticism is that it lacks interpretability, meaning that it can be difficult to understand how the model arrived at its predictions or decisions.[1]
References
- ^ a b 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.
{{cite journal}}: CS1 maint: PMC format (link) - ^ a b O'Reilly, Randall C. (1996-07-01). "Biologically Plausible Error-Driven Learning Using Local Activation Differences: The Generalized Recirculation Algorithm". Neural Computation. 8 (5): 895–938. doi:10.1162/neco.1996.8.5.895. ISSN 0899-7667.
- ^ a b c d e f Mohammad, Saif, and Ted Pedersen. "Combining lexical and syntactic features for supervised word sense disambiguation." Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) at HLT-NAACL 2004. 2004. APA
- ^ a b Florian, Radu, et al. "Named entity recognition through classifier combination." Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003. 2003.
- ^ a b Rozovskaya, Alla, and Dan Roth. "Grammatical error correction: Machine translation and classifiers." Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.
- ^ a b Iosif, Elias; Klasinas, Ioannis; Athanasopoulou, Georgia; Palogiannidi, Elisavet; Georgiladakis, Spiros; Louka, Katerina; Potamianos, Alexandros (2018-01-01). "Speech understanding for spoken dialogue systems: From corpus harvesting to grammar rule induction". Computer Speech & Language. 47: 272–297. doi:10.1016/j.csl.2017.08.002. ISSN 0885-2308.