Error-driven learning

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Error-driven learning is a type of reinforcement learning method. This method tweaks a model’s parameters based on the difference between the proposed and actual results. These models stand out as they depend on environmental feedback instead of explicit labels or categories.[1] They are based on the idea that language acquisition involves the minimization of the prediction error (MPSE).[2] By leveraging these prediction errors, the models consistently refine expectations and decrease computational complexity. Typically, these algorithms are operated by the GeneRec algorithm.[3]

Error-driven learning has widespread applications in cognitive sciences and computer vision. These methods have also found successful application in natural language processing (NLP), including areas like part-of-speech tagging,[4] parsing[4] named entity recognition (NER),[5] machine translation (MT),[6] speech recognition (SR)[4] and dialogue systems.[7]

  1. ^ 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.
  2. ^ 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.
  3. ^ Cite error: The named reference :6 was invoked but never defined (see the help page).
  4. ^ a b c 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
  5. ^ Florian, Radu, et al. "Named entity recognition through classifier combination." Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003. 2003.
  6. ^ 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.
  7. ^ 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.