03524nam a22005655i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001500172072001700187072002300204072001600227100008400243245012000327250001800447264007500465300006200540336002600602337002600628338003600654347002400690490006600714505032800780520102301108650005902131650002902190650003602219650003102255650003002286650003202316650004602348650002902394650003602423650003102459650003002490650003202520700008702552710003402639773002602673776003602699776003602735776003602771830007302807856004602880912001502926999001702941978-3-031-43260-6DE-He21320251030110827.0cr nn 008mamaa231030s2024    sz |    s    |||| 0|eng d  a97830314326069978-3-031-43260-67 a10.1007/978-3-031-43260-62doi 4aQA76.9.N38 7aUYQL2bicssc 7aCOM0730002bisacsh 7aUYQL2thema1 aBeinborn, Lisa.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut96685310aCognitive Plausibility in Natural Language Processingh[electronic resource] /cby Lisa Beinborn, Nora Hollenstein.  a1st ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXI, 158 p. 3 illus., 2 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSynthesis Lectures on Human Language Technologies,x1947-40590 aAcknowledgments -- Chapter 1 Introduction -- Chapter 2 Foundations of Language Modeling -- Chapter 3 Cognitive Signals of Language Processing -- Chapter 4 Behavioral Patterns -- Chapter 5 Representational Structure -- Chapter 6 Procedural Strategies -- Chapter 7 Towards Cognitively More Plausible Models -- Bibliography. .  aThis book explores the cognitive plausibility of computational language models and why it’s an important factor in their development and evaluation. The authors present the idea that more can be learned about cognitive plausibility of computational language models by linking signals of cognitive processing load in humans to interpretability methods that allow for exploration of the hidden mechanisms of neural models. The book identifies limitations when applying the existing methodology for representational analyses to contextualized settings and critiques the current emphasis on form over more grounded approaches to modeling language. The authors discuss how novel techniques for transfer and curriculum learning could lead to cognitively more plausible generalization capabilities in models. The book also highlights the importance of instance-level evaluation and includes thorough discussion of the ethical considerations that may arise throughout the various stages of cognitive plausibility research. . 0aNatural language processing (Computer science).965131 0aMachine learning.955059 0aArtificial intelligence.958441 0aComputational linguistics. 0aCognitive science.966854 0aApplied linguistics.96685514aNatural Language Processing (NLP).96616424aMachine Learning.95505924aArtificial Intelligence.95844124aComputational Linguistics.24aCognitive Science.96685624aApplied Linguistics.9668571 aHollenstein, Nora.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9668582 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303143259008iPrinted edition:z978303143261308iPrinted edition:z9783031432620 0aSynthesis Lectures on Human Language Technologies,x1947-405996685940uhttps://doi.org/10.1007/978-3-031-43260-6  aZDB-2-SXSC  c56467d56467