Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Emmanuele Chersoni, Cassandra Jacobs, Alessandro Lenci, Tal Linzen, Laurent Prévot, Enrico Santus (Editors)


Anthology ID:
W19-29
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
CMCL | NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/W19-29
DOI:
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PDF:
https://aclanthology.org/W19-29.pdf

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Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Emmanuele Chersoni | Cassandra Jacobs | Alessandro Lenci | Tal Linzen | Laurent Prévot | Enrico Santus

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Priming vs. Inhibition of Optional Infinitival to
Robin Melnick | Thomas Wasow

The word to that precedes verbs in English infinitives is optional in at least two environments : in what Wasow et al. (2015) previously called the do-be construction, and in the complement of help, which we explore in the present work. In the do-be construction, Wasow et al. found that a preceding infinitival to increases the use of following optional to, but the use of to in the complement of help is reduced following to help. We examine two hypotheses regarding why the same function word is primed by prior use in one construction and inhibited in another. We then test predictions made by the two hypotheses, finding support for one of them.

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Simulating Spanish-English Code-Switching : El Modelo Est Generating Code-SwitchesSpanish-English Code-Switching: El Modelo Está Generating Code-Switches
Chara Tsoukala | Stefan L. Frank | Antal van den Bosch | Jorge Valdés Kroff | Mirjam Broersma

Multilingual speakers are able to switch from one language to the other (code-switch) between or within sentences. Because the underlying cognitive mechanisms are not well understood, in this study we use computational cognitive modeling to shed light on the process of code-switching. We employed the Bilingual Dual-path model, a Recurrent Neural Network of bilingual sentence production (Tsoukala et al., 2017), and simulated sentence production in simultaneous Spanish-English bilinguals. Our first goal was to investigate whether the model would code-switch without being exposed to code-switched training input. The model indeed produced code-switches even without any exposure to such input and the patterns of code-switches are in line with earlier linguistic work (Poplack,1980). The second goal of this study was to investigate an auxiliary phrase asymmetry that exists in Spanish-English code-switched production. Using this cognitive model, we examined a possible cause for this asymmetry. To our knowledge, this is the first computational cognitive model that aims to simulate code-switched sentence production.

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A Modeling Study of the Effects of Surprisal and Entropy in Perceptual Decision Making of an Adaptive Agent
Pyeong Whan Cho | Richard Lewis

Processing difficulty in online language comprehension has been explained in terms of surprisal and entropy reduction. Although both hypotheses have been supported by experimental data, we do not fully understand their relative contributions on processing difficulty. To develop a better understanding, we propose a mechanistic model of perceptual decision making that interacts with a simulated task environment with temporal dynamics. The proposed model collects noisy bottom-up evidence over multiple timesteps, integrates it with its top-down expectation, and makes perceptual decisions, producing processing time data directly without relying on any linking hypothesis. Temporal dynamics in the task environment was determined by a simple finite-state grammar, which was designed to create the situations where the surprisal and entropy reduction hypotheses predict different patterns. After the model was trained to maximize rewards, the model developed an adaptive policy and both surprisal and entropy effects were observed especially in a measure reflecting earlier processing.

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Dependency Parsing with your Eyes : Dependency Structure Predicts Eye Regressions During Reading
Alessandro Lopopolo | Stefan L. Frank | Antal van den Bosch | Roel Willems

Backward saccades during reading have been hypothesized to be involved in structural reanalysis, or to be related to the level of text difficulty. We test the hypothesis that backward saccades are involved in online syntactic analysis. If this is the case we expect that saccades will coincide, at least partially, with the edges of the relations computed by a dependency parser. In order to test this, we analyzed a large eye-tracking dataset collected while 102 participants read three short narrative texts. Our results show a relation between backward saccades and the syntactic structure of sentences.

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Testing a Minimalist Grammar Parser on Italian Relative Clause AsymmetriesMinimalist Grammar Parser on Italian Relative Clause Asymmetries
Aniello De Santo

Stabler’s (2013) top-down parser for Minimalist grammars has been used to account for off-line processing preferences across a variety of seemingly unrelated phenomena cross-linguistically, via complexity metrics measuring memory burden. This paper extends the empirical coverage of the model by looking at the processing asymmetries of Italian relative clauses, as I discuss the relevance of these constructions in evaluating plausible structure-driven models of processing difficulty.

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The Development of Abstract Concepts in Children’s Early Lexical Networks
Abdellah Fourtassi | Isaac Scheinfeld | Michael Frank

How do children learn abstract concepts such as animal vs. artifact? Previous research has suggested that such concepts can partly be derived using cues from the language children hear around them. Following this suggestion, we propose a model where we represent the children’ developing lexicon as an evolving network. The nodes of this network are based on vocabulary knowledge as reported by parents, and the edges between pairs of nodes are based on the probability of their co-occurrence in a corpus of child-directed speech. We found that several abstract categories can be identified as the dense regions in such networks. In addition, our simulations suggest that these categories develop simultaneously, rather than sequentially, thanks to the children’s word learning trajectory which favors the exploration of the global conceptual space.

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Verb-Second Effect on Quantifier Scope Interpretation
Asad Sayeed | Matthias Lindemann | Vera Demberg

Sentences like Every child climbed a tree have at least two interpretations depending on the precedence order of the universal quantifier and the indefinite. Previous experimental work explores the role that different mechanisms such as semantic reanalysis and world knowledge may have in enabling each interpretation. This paper discusses a web-based task that uses the verb-second characteristic of German main clauses to estimate the influence of word order variation over world knowledge.

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Neural Models of the Psychosemantics of ‘Most’
Lewis O’Sullivan | Shane Steinert-Threlkeld

How are the meanings of linguistic expressions related to their use in concrete cognitive tasks? Visual identification tasks show human speakers can exhibit considerable variation in their understanding, representation and verification of certain quantifiers. This paper initiates an investigation into neural models of these psycho-semantic tasks. We trained two types of network a convolutional neural network (CNN) model and a recurrent model of visual attention (RAM) on the most verification task from Pietroski2009, manipulating the visual scene and novel notions of task duration. Our results qualitatively mirror certain features of human performance (such as sensitivity to the ratio of set sizes, indicating a reliance on approximate number) while differing in interesting ways (such as exhibiting a subtly different pattern for the effect of image type). We conclude by discussing the prospects for using neural models as cognitive models of this and other psychosemantic tasks.

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The Role of Utterance Boundaries and Word Frequencies for Part-of-speech Learning in Brazilian Portuguese Through Distributional AnalysisBrazilian Portuguese Through Distributional Analysis
Pablo Picasso Feliciano de Faria

In this study, we address the problem of part-of-speech (or syntactic category) learning during language acquisition through distributional analysis of utterances. A model based on Redington et al.’s (1998) distributional learner is used to investigate the informativeness of distributional information in Brazilian Portuguese (BP). The data provided to the learner comes from two publicly available corpora of child directed speech. We present preliminary results from two experiments. The first one investigates the effects of different assumptions about utterance boundaries when presenting the input data to the learner. The second experiment compares the learner’s performance when counting contextual words’ frequencies versus just acknowledging their co-occurrence with a given target word. In general, our results indicate that explicit boundaries are more informative, frequencies are important, and that distributional information is useful to the child as a source of categorial information. These results are in accordance with Redington et al.’s findings for English.

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Using Grounded Word Representations to Study Theories of Lexical Concepts
Dylan Ebert | Ellie Pavlick

The fields of cognitive science and philosophy have proposed many different theories for how humans represent concepts. Multiple such theories are compatible with state-of-the-art NLP methods, and could in principle be operationalized using neural networks. We focus on two particularly prominent theoriesClassical Theory and Prototype Theoryin the context of visually-grounded lexical representations. We compare when and how the behavior of models based on these theories differs in terms of categorization and entailment tasks. Our preliminary results suggest that Classical-based representations perform better for entailment and Prototype-based representations perform better for categorization. We discuss plans for additional experiments needed to confirm these initial observations.