Papers by Coleman Haley
A Grounded Typology of Word Classes (2025.naacl-long)
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| Challenge: | Using captioned images, we can quantify language function and semantics using a grounded typology approach . linguistic typology is the study of patterns and variation across the world's languages . |
| Approach: | They propose a grounded typology approach that uses images captioned across languages to quantify meaning and semantics. |
| Outcome: | The proposed approach can quantify language function and semantics using images captioned across languages. |
Morphology Matters: A Multilingual Language Modeling Analysis (2021.tacl-1)
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| Challenge: | Existing studies on inflectional morphology disagree on whether or not it makes languages harder to model. |
| Approach: | They propose to use a corpus of 145 Bible translations in 92 languages to investigate whether inflectional morphology makes languages harder to model. |
| Outcome: | The proposed model trains with linguistically motivated subword segmentation strategies and reduces the impact of morphology on language modeling. |
Is Information Density Uniform when Utterances are Grounded on Perception and Discourse? (2026.eacl-long)
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| Challenge: | Existing studies on the distribution of information in visually grounded contexts have focused on text-only inputs. |
| Approach: | They propose to use multilingual vision-and-language models to estimate surprisal . they find grounding on perception increases uniformity across typologically diverse languages . |
| Outcome: | The proposed hypothesis is tested in visual-language models over 30 languages and 13 storytelling languages . the results show grounding on perception increases uniformity across languages compared to text-only settings . |
Invertible Tree Embeddings using a Cryptographic Role Embedding Scheme (2020.coling-main)
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| Challenge: | Unlike previous attempts, this method does not come at the cost of intractable representation size; it works well when there is sufficient randomness in the representation scheme for simple data and providing an upper bound on its error. |
| Approach: | They propose a method for embedding trees in a vector space based on Tensor-Product Representations (TPRs) that allows for inversion: the retrieval of the original tree structure and nodes from the vectorial embeddment. |
| Outcome: | The proposed method can provide invertibility with error 1% that previous methods would require 8.6 1057 dimensions to represent. |