Papers by Elise Darragh-Ford
Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models (2022.acl-long)
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Mark Chu, Bhargav Srinivasa Desikan, Ethan Nadler, Donald Ruggiero Lo Sardo, Elise Darragh-Ford, Douglas Guilbeault
| Challenge: | Existing words represent an extremely small fraction of the space of possible character level n-grams (word forms) yet, a plethora of insights into language learning have emerged from inquiries into language beyond extant words, such as the grammatical errors and inference patterns children exhibit when distinguishing extant word from non-linguistic auditory signals. |
| Approach: | They propose that random character n-grams provide a novel context for studying word meaning both within and beyond extant language. |
| Outcome: | The proposed model identifies an axis in its high-dimensional embedding space that separates these classes of n-grams from other classes of characters and relates to structure within extant language, including word part-of-speech, morphology, and concept concreteness. |