Harald Baayen

Hassan Shahmohammadi, Maria Heitmeier, Elnaz Shafaei-Bajestan et al.

Grounding language in vision is an active field of research seeking to construct cognitively plausible word and sentence representations by incorporating perceptual knowledge from vision into text-based representations. Despite many attempts at language grounding, achieving an optimal equilibrium between textual representations of the language and our embodied experiences remains an open field. Some common concerns are the following. Is visual grounding advantageous for abstract words, or is its effectiveness restricted to concrete words? What is the optimal way of bridging the gap between text and vision? To what extent is perceptual knowledge from images advantageous for acquiring high-quality embeddings? Leveraging the current advances in machine learning and natural language processing, the present study addresses these questions by proposing a simple yet very effective computational grounding model for pre-trained word embeddings. Our model effectively balances the interplay between language and vision by aligning textual embeddings with visual information while simultaneously preserving the distributional statistics that characterize word usage in text corpora. By applying a learned alignment, we are able to indirectly ground unseen words including abstract words. A series of evaluations on a range of behavioural datasets shows that visual grounding is beneficial not only for concrete words but also for abstract words, lending support to the indirect theory of abstract concepts. Moreover, our approach offers advantages for contextualized embeddings, such as those generated by BERT, but only when trained on corpora of modest, cognitively plausible sizes. Code and grounded embeddings for English are available at https://github.com/Hazel1994/Visually_Grounded_Word_Embeddings_2.

Hassan Shahmohammadi, Maria Heitmeier, Elnaz Shafaei-Bajestan et al.

Current word embedding models despite their success, still suffer from their lack of grounding in the real world. In this line of research, Gunther et al. 2022 proposed a behavioral experiment to investigate the relationship between words and images. In their setup, participants were presented with a target noun and a pair of images, one chosen by their model and another chosen randomly. Participants were asked to select the image that best matched the target noun. In most cases, participants preferred the image selected by the model. Gunther et al., therefore, concluded the possibility of a direct link between words and embodied experience. We took their experiment as a point of departure and addressed the following questions. 1. Apart from utilizing visually embodied simulation of given images, what other strategies might subjects have used to solve this task? To what extent does this setup rely on visual information from images? Can it be solved using purely textual representations? 2. Do current visually grounded embeddings explain subjects' selection behavior better than textual embeddings? 3. Does visual grounding improve the semantic representations of both concrete and abstract words? To address these questions, we designed novel experiments by using pre-trained textual and visually grounded word embeddings. Our experiments reveal that subjects' selection behavior is explained to a large extent based on purely text-based embeddings and word-based similarities, suggesting a minor involvement of active embodied experiences. Visually grounded embeddings offered modest advantages over textual embeddings only in certain cases. These findings indicate that the experiment by Gunther et al. may not be well suited for tapping into the perceptual experience of participants, and therefore the extent to which it measures visually grounded knowledge is unclear.

Harald Baayen, Kristian Berg, Maziyah Mohamed

In this study, we approach morphological productivity from two perspectives: a cognitive-computational perspective, and a diachronic perspective zooming in on an actual speaker, Thomas Mann. For developing the first perspective, we make use of a cognitive computational model of the mental lexicon, the discriminative lexicon model. For computational mappings between form and meaning to be productive, in the sense that novel, previously unencountered words, can be understood and produced, there must be systematicities between the form space and the semantic space. If the relation between form and meaning would be truly arbitrary, a model could memorize form and meaning pairings, but there is no way in which the model would be able to generalize to novel test data. For Finnish nominal inflection, Malay derivation, and English compounding, we explore, using the Discriminative Lexicon Model as a computational tool, to trace differences in the degree to which inflectional and word formation patterns are productive. We show that the DLM tends to associate affix-like sublexical units with the centroids of the embeddings of the words with a given affix. For developing the second perspective, we study how the intake and output of one prolific writer, Thomas Mann, changes over time. We show by means of an examination of what Thomas Mann is likely to have read, and what he wrote, that the rate at which Mann produces novel derived words is extremely low. There are far more novel words in his input than in his output. We show that Thomas Mann is less likely to produce a novel derived word with a given suffix the greater the average distance is of the embeddings of all derived words to the corresponding centroid, and discuss the challenges of using speaker-specific embeddings for low-frequency and novel words.

Kaidi Lõo, Arvi Tavast, Maria Heitmeier et al.

This study investigates lexical processing in Estonian. A large-scale single-subject experiment is reported that combines the word naming task with eye-tracking. Five response variables (first fixation duration, total fixation duration, number of fixations, word naming latency, and spoken word duration) are analyzed with the generalized additive model. Of central interest is the question of whether measures for lexical processing generated by a computational model of the mental lexicon (the Discriminative Lexicon Model, DLM) are predictive for these response variables, and how they compare to classical predictors such as word frequency, neighborhood size, and inflectional paradigm size. Computational models were implemented both with linear and deep mappings. Central findings are, first, that DLM-based measures are powerful predictors for lexical processing, second, that DLM-measures using deep learning are not necessarily more precise predictors of lexical processing than DLM-measures using linear mappings, third, that classical predictors tend to provide somewhat more precise fits compared to DLM-based predictors (except for total fixation duration, where the two provide equivalent goodness of fit), and fourth, that in the naming task lexical variables are not predictive for first fixation duration and the total number of fixations. As the DLM works with mappings from form to meaning, the predictivity of DLM-based measures for total fixation duration, naming latencies, and spoken word duration indicates that meaning is heavily involved in the present word naming task.