- The paper introduces "groundedness", a new metric derived from multimodal models comparing image captioning and language models, to quantify semantic contentfulness of word classes across languages.
- Using groundedness, the study reveals that functional word classes have more semantic content than traditionally assumed and confirms a universal groundedness hierarchy where nouns > adjectives/verbs > functional categories.
- This grounded typology approach offers a data-driven method for assessing word class semantics, providing a dataset and paving the way for future research in computational linguistics and typological studies.
A Grounded Typology of Word Classes: A Multimodal Perspective
The concept of language typology has long intrigued linguists, focusing on the paper of patterns and variations across global languages. This paper introduces an innovative, grounded approach to language typology by leveraging multimodal data—specifically, images—as a language-agnostic representation of meaning. This approach allows researchers to quantify the relationship between linguistic form and semantic function across multiple languages using multilingual multimodal LLMs.
Groundedness and Its Application
The paper presents "groundedness" as a novel metric derived from an information-theoretic perspective, which measures contextual semantic contentfulness. This is quantified as a difference in surprisal between an image captioning model and a LLM. Such a measure is illustrative of how much content a word conveys about an image in various languages. As a proof-of-concept, groundedness is applied to the typology of word classes to explore contentfulness asymmetry between different grammatical categories.
The research finds notable asymmetries in semantic content conveyed by functional and lexical word classes across languages. Contrary to traditional linguistic views that functional word classes impart little content, the groundedness measure suggests these classes possess noteworthy semantic significance. Additionally, the paper identifies universal patterns in the hierarchy of groundedness, with nouns being more grounded than adjectives and verbs and provides a dataset of groundedness scores across 30 languages.
Methodology and Results
The groundedness measure is made possible by multimodal neural networks capable of processing both text and images. This approach creates a bridge between linguistic meaning and perceptual stimuli, allowing for cross-linguistic comparison. The paper's analysis reveals that word classes associated with high semantic content, such as nouns and adjectives, exhibit higher groundedness scores than functional categories like conjunctions and adpositions. Interestingly, the paper challenges conventional assumptions by showing that grammatical classes can convey semantic meaning.
Qualitative analysis across datasets like Crossmodal-3600, COCO-35L, and Multi30K highlights consistency in the groundedness hierarchy across typologically diverse languages, validating the proposed method. The measure also shows partial correlation with psycholinguistic concreteness norms in English, affirming the broader applicability of groundedness in understanding language.
Implications and Future Directions
From a theoretical standpoint, this paper offers a quantifiable method for assessing the semantic functions of word classes, contributing robust evidence to typological discussions. Practically, the dataset and the proposed method pave the way for future research to explore the semantic nuances of less studied languages, potentially revisiting established linguistic classifications.
The prospect of using multimodal models opens new avenues for computational linguistics and typology, suggesting the potential for such models as quantitative tools in understanding language function and semantics. Future research could expand the application of groundedness to explore non-canonical word classes, variations in inflectional and derivational morphology, or even the lexicalization processes across languages.
In conclusion, the grounded typology approach offers a promising paradigm for linguistic research, extending traditional typology by providing a data-driven, empirical measure of semantic contentfulness across languages. This paper's insights lay the groundwork for more nuanced examinations of language universals and typological claims, underscoring the transformative power of integrating multimodal data into linguistic analysis.