User:An anonymous user with secrets/sandbox/Error-driven learning
Error-driven learning is a type of reinforcement learning algorithm. It 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
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
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.[4]
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.