Estonian case inflection made simple. A case study in Word and Paradigm morphology with Linear Discriminative Learning.

Chuang, Yu‐Ying; Lõo, Kaidi; Blevins, James P.; Baayen, R. Harald

doi:10.31234/osf.io/hdftz

Cited by 24 publications

(39 citation statements)

References 21 publications

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“…We note here that computationally, our study offers two innovations to the theory of the Discriminative Lexicon, as developed in Baayen et al (2019c); Chuang et al (2019). First, whereas in previous work, the focus was on the end-state of learning, in the present study, we have demonstrated the potential of the learning rule of Widrow and Hoff (Widrow and Hoff, 1960) to study the trajectory of learning (see Milin et al, 2020, for this learning rule, related learning strategies, and efficient implementation).…”

Section: Englishmentioning

confidence: 65%

“…By contrast, it is in production that we see errors arise as new languages are learned, resulting in imperfect learning that may persist even at the end-state. That comprehension is ahead of production was also observed by Chuang et al (2019) for Estonian noun inflection, but their study only considered the end-state of learning. Here, we replicated their result, and at the same time extend it to incremental learning.…”

Section: Englishmentioning

confidence: 86%

“…For a different approach to morphology that builds on linguistic domain knowledge seeBaayen et al (2019c) andChuang et al (2019) …”

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confidence: 99%

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Bilingual and multilingual mental lexicon: a modeling study with Linear Discriminative Learning

Chuang¹,

Bell²,

Isabelle³

et al. 2020

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This study addresses the question of whether there is anything special about learning a third language, as compared to learning a second language, just by virtue of the third language being the third language acquired, and independently of the specific properties of the third language. We used computational modeling to explore this question for the learning of a small vocabulary of some 400 words, with English as L1, German or Mandarin as L2, and Mandarin and alternatively Dutch, as L3. For computational modeling, we made use of the mathematical framework of linear discriminative learning, which we extended with the learning rule of Widrow-Hoff to enable the modeling of incremental learning of the mappings between form and meaning when words' meanings are represented by vectors of real numbers (embeddings) rather than by abstract symbolic units. A series of simulation experiments covering single-language learning, bilingual learning, and finally trilingual learning, clarified that within the framework of discrimination learning, within-language homophones give rise to frailty in comprehension that in turn for production gives rise to semantic errors in L1, and language intrusions in L2 and L3. Our model correctly predicts production to lag behind comprehension in learning, and it clarified that, within the boundaries of discrimination learning, the properties of the L3 crucially determine whether L3 learning appears to involve a language that is `dormant' with respect to L1 and L2. Qualitatively surprisingly different patterns of acquisition of the L3, and its interactions with L1 and L2, can arise in our simulations without any changes in the mathematics driving learning. Our simulations also show that when words' forms incorporate not only segmental but also suprasegmental information, the nature of errors that arise in production changes. In the general discussion, we reflect on the implications of our findings for the question of what is special about multilingualism.

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Section: Englishmentioning

confidence: 65%

Section: Englishmentioning

confidence: 86%

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Bilingual and multilingual mental lexicon: a modeling study with Linear Discriminative Learning

Chuang¹,

Bell²,

Isabelle³

et al. 2020

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“…In both Baayen et al (2019) and the present study, we used the Frequency Band Summary (FBS) features developed by Arnold et al (2017), which will be described in more detail below (Section 4.2.3). Similar to the visual vector, the length of an auditory cue vector is equal to the number of different FBS features in the training data, and matrix of neighboring orthographic and phonological information and usually work well not only for English (Milin et al, 2017;Baayen et al, 2019) but also for many typologically different languages such as Latin (Baayen et al, 2018a) and Estonian (Chuang et al, 2019). However, the optimal grain size of form representations can sometimes still be language-dependent.…”

Section: A Blueprint Of the Mental Lexicon Using Linear Discriminativmentioning

confidence: 99%

The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using Linear Discriminative Learning

Chuang¹,

Voller²,

Shafaei-Bajestan³

et al. 2020

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Pseudowords have long served as key tools in psycholinguistic investigations of the lexicon. A common assumption underlying the use of pseudowords is that they are devoid of meaning: Comparing words and pseudowords may then shed light on how meaningful linguistic elements are processed differently from meaningless sound strings.However, pseudowords may in fact carry meaning. On the basis of a computational model of lexical processing, Linear Discriminative Learning (LDL Baayen et al., 2019), we compute numeric vectors representing the semantics of pseudowords. We demonstrate that quantitative measures gauging the semantic neighborhoods of pseudowords predict reaction times in the Massive Auditory Lexical Decision (MALD) database (Tucker et al., 2018). We also show that the model successfully predicts the acoustic durations of pseudowords. Importantly, model predictions hinge on the hypothesis that the mechanisms underlying speech production and comprehension interact. Thus, pseudowords emerge as an outstanding tool for gauging the resonance between production and comprehension.Many pseudowords in the MALD database contain inflectional suffixes. Unlike many contemporary models, LDL captures the semantic commonalities of forms sharing inflectional exponents without using the linguistic construct of morphemes. We discuss methodological and theoretical implications for models of lexical processing and morphological theory. The results of this study, complementing those on real words reported in Baayen et al. (2019), thus provide further evidence for the usefulness of LDL both as a cognitive model of the mental lexicon, and as a tool for generating new quantitative measures that are predictive for human lexical processing.

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“…In other words, triphones with a value below a threshold θ in a predicted form vectorĉ are not taken into account when constructing the graph. As shown by Baayen et al (2018) and Chuang et al (2019), this algorithm succeeds in predicting words' forms with high accuracy.…”

Section: The Discriminative Lexiconmentioning

confidence: 71%

Simulating phonological and semantic impairment of English tense inflection with linear discriminative learning

Heitmeier

Baayen

2020

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This study applies the computational theory of the ‘discriminative lexicon’ (Baayen, Chuang, Blevins, 2019) to the modeling of the production of English verbs in aphasic speech. Under semantic impairment, speakers have been reported to have greater difficulties with irregular verbs, whereas speakers with phonological impairment are described as having greater problems with regulars. Joanisse and Seidenberg (1999) were able to model this dissociation, but only by adding noise to the semantic units of their model. We report two simulation studies in which topographically coherent regions of phonological and semantic networks were selectively damaged. Our model replicated the main findings, including the high variability in the consequences of brain lesions for speech production. Importantly, our model generated these results without having to lesion the semantic system more than the phonological system. The model’s success hinges on the use of a corpus-based distributional vector space for representing verbs’ meanings. Irregular verbs have denser semantic neighborhoods than do regular verbs (Baayen and Moscoso del Prado Martín, 2005). In our model this renders irregular verbs more fragile under semantic impairment. These results provide further support for the central idea underlying the discriminative lexicon: that behavioral patterns can, to a considerable extent, be understood as emerging from the distributional properties of a language and basic principles of human learning.

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Estonian case inflection made simple. A case study in Word and Paradigm morphology with Linear Discriminative Learning.

Cited by 24 publications

References 21 publications

Bilingual and multilingual mental lexicon: a modeling study with Linear Discriminative Learning

Bilingual and multilingual mental lexicon: a modeling study with Linear Discriminative Learning

The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using Linear Discriminative Learning

Simulating phonological and semantic impairment of English tense inflection with linear discriminative learning

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