Papers by Daniel King
Strong Baselines for Complex Word Identification across Multiple Languages (N19-1)
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Pierre Finnimore, Elisabeth Fritzsch, Daniel King, Alison Sneyd, Aneeq Ur Rehman, Fernando Alva-Manchego, Andreas Vlachos
| Challenge: | Complex Word Identification (CWI) is the task of identifying which words or phrases in a sentence are difficult to understand by a specific type of reader. |
| Approach: | They propose to use monolingual and cross-lingual CWI models to make predictions for languages not seen during training. |
| Outcome: | The proposed models perform as well as (or better than) most models submitted to the latest CWI Shared Task. |
ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts (2022.emnlp-demos)
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Sonia Murthy, Kyle Lo, Daniel King, Chandra Bhagavatula, Bailey Kuehl, Sophie Johnson, Jonathan Borchardt, Daniel Weld, Tom Hope, Doug Downey
| Challenge: | Current systems that automatically define unfamiliar terms only surface a single "best" description for all users, which may not be accessible for all readers, given varying background knowledge. |
| Approach: | They propose an end-to-end system that generates sets of descriptions of scientific concepts . ACCoRD corpus includes 1,275 labeled contexts and 1,787 expert-authored concept descriptions . |
| Outcome: | The proposed system produces diverse descriptions of concepts in terms of reference concepts. |
Pretrained Language Models for Sequential Sentence Classification (D19-1)
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| Challenge: | Recent successful models for document-level understanding have used hierarchical encoding and CRFs to capture dependencies between subsequent labels. |
| Approach: | They propose a pretrained language model that captures contextual dependencies without hierarchical encoding nor a CRF. |
| Outcome: | The proposed model captures contextual dependencies without hierarchical encoding nor a CRF on four datasets, including a new dataset of structured scientific abstracts. |