Papers by Brian DuSell
Efficiency through Auto-Sizing: Notre Dame NLP’s Submission to the WNGT 2019 Efficiency Task (D19-56)
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| Challenge: | Notre Dame Natural Language Processing group applied auto-sizing to the Transformer network to reduce the number of parameters in the model. |
| Approach: | They investigated the impact of auto-sizing on the Transformer network by applying a method to inducing sparsity in parameters. |
| Outcome: | The proposed method eliminated more than 25% of the model’s parameters while suffering a decrease of only 1.1 BLEU. |
PILA: A Historical-Linguistic Dataset of Proto-Italic and Latin (2024.lrec-main)
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| Challenge: | Historical linguists hypothesize systems of sound change to explain the evolution of language over time, but the evidence is limited. |
| Approach: | They propose a dataset that consists of roughly 3,000 pairs of forms from Proto-Italic and Latin. |
| Outcome: | The proposed dataset enables historical linguists to enhance other datasets by enhancing them with the existing datasets. |
Information Locality as an Inductive Bias for Neural Language Models (2025.acl-long)
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| Challenge: | Inductive biases are inherent in every machine learning system, argues a new study . m-local entropy measures how well symbols disambiguate the next symbol . |
| Approach: | They propose a framework that captures local uncertainty of a language by quantifying how effectively preceding symbols disambiguate the next symbol. |
| Outcome: | The proposed framework captures the local uncertainty of a language by quantifying how effectively symbols disambiguate the next symbol. |
On the Proper Treatment of Tokenization in Psycholinguistics (2024.emnlp-main)
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| Challenge: | Language models are used in computational psycholinguistics to test theories that relate the surprisal of a region of interest to its cognitive cost experienced by readers. |
| Approach: | They propose to marginalize token-level language models into character-level ones before they are used in psycholinguistic studies. |
| Outcome: | The proposed model over token strings is better than character-level model, the authors show . the proposed model marginalizes token-level models into character-based models before they are used in psycholinguistic studies. |
Algorithms for Weighted Pushdown Automata (2022.emnlp-main)
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| Challenge: | Existing dynamic programming algorithms for PDAs often resort to a PDA-to-CFG conversion. |
| Approach: | They propose to use a pushdown automaton to reduce the space requirements by a factor of |Gamma| or the runtime by reducing the number of states. |
| Outcome: | The proposed algorithms reduce the space requirements by a factor of |Gamma| or reduce the runtime by fewer states. |