Coleman Haley
colemanhaley.bsky.social
Coleman Haley
@colemanhaley.bsky.social
NLP PhD candidate @ University of Edinburgh

Computational Linguistics | Typology | Morphology | Multimodal NLP | Cognitive Science

(Interpretability + Neurosymbolic models sometimes)
📝 Read the full paper: arxiv.org/pdf/2412.10369
📊 Explore our groundedness dataset across 30 languages: osf.io/bdhna/

This is just the beginning—multimodal models are a powerful tool for exploring linguistic form and meaning.
#Linguistics #Typology #MultimodalML #NLP
arxiv.org
December 20, 2024 at 5:05 PM
10/ We find our measures diverge from related psycholinguistic norms (concreteness and imageability), but this divergence is largely due to our measure's informativity dimension.
December 20, 2024 at 5:05 PM
9/ While we expect lexical classes to be grounded, we find functional classes—traditionally viewed as “grammatical” or “abstract”—also carry semantic content.

For example, determiners like "der" or "une" still contribute to meaning, challenging common assumptions in linguistics.
December 20, 2024 at 5:05 PM
8/ Across 30 typologically diverse languages, we find a cline between Nouns > Adjectives > Verbs.

This corroborates ideas from cognitive linguistics that suggest these classes lie in a continuum.
December 20, 2024 at 5:05 PM
7/ To validate our measure we look at the lexical-functional distinction in word classes:

Lexical classes: nouns, verbs, adjectives—contentful words.
Functional classes: prepositions, determiners—“grammatical” words.

How universal is this distinction? Is there a clear line?
December 20, 2024 at 5:05 PM
6/ Groundedness turns out to be the *decrease in surprisal* of a word when we see the image it refers to! But ensuring comparability is tricky (see paper for details).
December 20, 2024 at 5:05 PM
5/ Our use of images makes groundedness straightforward to compute. We need only the log probabilities from:
- a language model p(word | context
- an image captioning model p(word | context, meaning)
December 20, 2024 at 5:05 PM
4/ We focus on the strength of association between a word and the meaning expressed: how contentful a word is. We express this in terms of the pointwise mutual information between a word and the meaning of an utterance. We call this measure *groundedness*.
December 20, 2024 at 5:05 PM
3/ / To align function across languages, we use image captions. The linguistic content of a caption aims to express the contents of an image. An image represents the state of the world in a language-neutral way, and so can serve as an imperfect proxy for meaning.
December 20, 2024 at 5:05 PM
2/ To study typological variation and universals, linguists must align and categorize languages. But this can be difficult and subjective.

In vowel typology, physical correlates are used as a proxy, allowing for empirical, objective comparisons. But what about language function?
December 20, 2024 at 5:05 PM