Stefan L. Frank

Also published as: Stefan Frank


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The Learnability of the Wh-Island Constraint in Dutch by a Long Short-Term Memory Network
Michelle Suijkerbuijk | Peter de Swart | Stefan L. Frank
Proceedings of the Society for Computation in Linguistics 2023


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Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge
Danny Merkx | Stefan Frank | Mirjam Ernestus
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Distributional semantic models capture word-level meaning that is useful in many natural language processing tasks and have even been shown to capture cognitive aspects of word meaning. The majority of these models are purely text based, even though the human sensory experience is much richer. In this paper we create visually grounded word embeddings by combining English text and images and compare them to popular text-based methods, to see if visual information allows our model to better capture cognitive aspects of word meaning. Our analysis shows that visually grounded embedding similarities are more predictive of the human reaction times in a large priming experiment than the purely text-based embeddings. The visually grounded embeddings also correlate well with human word similarity ratings. Importantly, in both experiments we show that the grounded embeddings account for a unique portion of explained variance, even when we include text-based embeddings trained on huge corpora. This shows that visual grounding allows our model to capture information that cannot be extracted using text as the only source of information.


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Human Sentence Processing: Recurrence or Attention?
Danny Merkx | Stefan L. Frank
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Recurrent neural networks (RNNs) have long been an architecture of interest for computational models of human sentence processing. The recently introduced Transformer architecture outperforms RNNs on many natural language processing tasks but little is known about its ability to model human language processing. We compare Transformer- and RNN-based language models’ ability to account for measures of human reading effort. Our analysis shows Transformers to outperform RNNs in explaining self-paced reading times and neural activity during reading English sentences, challenging the widely held idea that human sentence processing involves recurrent and immediate processing and provides evidence for cue-based retrieval.


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Less is Better: A cognitively inspired unsupervised model for language segmentation
Jinbiao Yang | Stefan L. Frank | Antal van den Bosch
Proceedings of the Workshop on the Cognitive Aspects of the Lexicon

Language users process utterances by segmenting them into many cognitive units, which vary in their sizes and linguistic levels. Although we can do such unitization/segmentation easily, its cognitive mechanism is still not clear. This paper proposes an unsupervised model, Less-is-Better (LiB), to simulate the human cognitive process with respect to language unitization/segmentation. LiB follows the principle of least effort and aims to build a lexicon which minimizes the number of unit tokens (alleviating the effort of analysis) and number of unit types (alleviating the effort of storage) at the same time on any given corpus. LiB’s workflow is inspired by empirical cognitive phenomena. The design makes the mechanism of LiB cognitively plausible and the computational requirement light-weight. The lexicon generated by LiB performs the best among different types of lexicons (e.g. ground-truth words) both from an information-theoretical view and a cognitive view, which suggests that the LiB lexicon may be a plausible proxy of the mental lexicon.


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Simulating Spanish-English Code-Switching: El Modelo Está Generating Code-Switches
Chara Tsoukala | Stefan L. Frank | Antal van den Bosch | Jorge Valdés Kroff | Mirjam Broersma
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

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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Dependency Parsing with your Eyes: Dependency Structure Predicts Eye Regressions During Reading
Alessandro Lopopolo | Stefan L. Frank | Antal van den Bosch | Roel Willems
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

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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Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)
Tomer Cagan | Stefan L. Frank | Reut Tsarfaty
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Opinionated Natural Language Generation (ONLG) is a new, challenging, task that aims to automatically generate human-like, subjective, responses to opinionated articles online. We present a data-driven architecture for ONLG that generates subjective responses triggered by users’ agendas, consisting of topics and sentiments, and based on wide-coverage automatically-acquired generative grammars. We compare three types of grammatical representations that we design for ONLG, which interleave different layers of linguistic information and are induced from a new, enriched dataset we developed. Our evaluation shows that generation with Relational-Realizational (Tsarfaty and Sima’an, 2008) inspired grammar gets better language model scores than lexicalized grammars ‘a la Collins (2003), and that the latter gets better human-evaluation scores. We also show that conditioning the generation on topic models makes generated responses more relevant to the document content.


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Generating Subjective Responses to Opinionated Articles in Social Media: An Agenda-Driven Architecture and a Turing-Like Test
Tomer Cagan | Stefan L. Frank | Reut Tsarfaty
Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media


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Word surprisal predicts N400 amplitude during reading
Stefan L. Frank | Leun J. Otten | Giulia Galli | Gabriella Vigliocco
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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Lexical surprisal as a general predictor of reading time
Irene Fernandez Monsalve | Stefan L. Frank | Gabriella Vigliocco
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics


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Uncertainty Reduction as a Measure of Cognitive Processing Effort
Stefan Frank
Proceedings of the 2010 Workshop on Cognitive Modeling and Computational Linguistics


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Book Reviews: From Molecule to Metaphor: A Neural Theory of Language, by Jerome A. Feldman
Stefan Frank
Computational Linguistics, Volume 33, Number 2, June 2007