Papers by Brian Mac Namee
Diverging Divergences: Examining Variants of Jensen Shannon Divergence for Corpus Comparison Tasks (2020.lrec-1)
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| Challenge: | Jensen-Shannon divergence (JSD) is a distribution similarity measurement widely used in natural language processing. |
| Approach: | They propose to use a weighted version of Jensen-Shannon divergence to compare corpora . they argue this weighting is unnecessary and can lead to misleading results . |
| Outcome: | The proposed weighting is unnecessary and can lead to misleading results. |
What Makes Pre-trained Language Models Better Zero-shot Learners? (2023.acl-long)
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| Challenge: | Current methods for prompt learning in zero-shot scenarios rely on a development set with sufficient human-annotated data to select the best-performing prompt template. |
| Approach: | They propose a method for screening reasonable prompt templates in zero-shot text classification using language discrepancy. |
| Outcome: | The proposed method improves prediction performance in a realistic zero-shot setting, eliminating the need for labelled examples. |