Papers by Mark Hopkins
Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples (P18-1)
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| Challenge: | Statistical parsers are often criticized for their performance outside of the domain they were trained on . we show that word representations reduce the need for domain adaptation when the target domain is syntactically similar to the source domain. |
| Approach: | They propose a way to adapt a parser to a syntactically similar target domain using partial annotations. |
| Outcome: | The proposed model increases the accuracy of a parser on the Wall Street Journal by 1.7% over the previous state-of-the-art model. |
On the Evaluation of Neural Selective Prediction Methods for Natural Language Processing (2023.acl-long)
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| Challenge: | Existing techniques for selective classification are lacking in the literature. |
| Approach: | They propose a methodological blueprint and a metric for calibrating confidence functions for selective prediction. |
| Outcome: | The proposed method improves on the GLUE benchmark and the proposed refinement metric provides a calibrated evaluation of confidence functions for selective prediction. |
Spot the Odd Man Out: Exploring the Associative Power of Lexical Resources (D18-1)
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| Challenge: | Existing word embeddings assign only one vector to each word, resulting in word disambiguation on smaller scales. |
| Approach: | They propose a task which aims to test different properties of word representations. |
| Outcome: | The proposed task is intuitive enough to annotate on a large scale while teasing out properties of popular lexical resources. |