Papers by Matthew Gormley
Learning Mutually Informed Representations for Characters and Subwords (2024.findings-naacl)
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| Challenge: | Pretrained language models rely on subword tokenization to process text as a sequence of subwords. |
| Approach: | They propose a character-subword language model that integrates character and subword modalities into one model. |
| Outcome: | The proposed model outperforms its backbone language models on English sequence labeling and classification tasks. |
MDACE: MIMIC Documents Annotated with Code Evidence (2023.acl-long)
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Hua Cheng, Rana Jafari, April Russell, Russell Klopfer, Edmond Lu, Benjamin Striner, Matthew Gormley
| Challenge: | Computer-Assisted Coding (CAC) systems are required to provide supporting textual evidence to justify billing codes. |
| Approach: | They propose a dataset for evidence/rationale extraction on an extreme multi-label classification task over long medical documents. |
| Outcome: | The proposed dataset can be used to evaluate evidence extraction methods for CAC systems, as well as the accuracy and interpretability of deep learning models for multi-label classification. |
On Efficiently Acquiring Annotations for Multilingual Models (2022.acl-short)
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| Challenge: | a recent study shows that joint learning across multiple languages performs better than the aforementioned approaches . traditional approaches to support NLP tasks require a lot of annotations to perform . a new approach is to train a model for each language with annotation budget divided equally among them . |
| Approach: | They propose a method for joint learning across multiple languages using a single model . they show that active learning provides additional, complementary benefits . |
| Outcome: | The proposed method outperforms other models on a diverse set of tasks . it can arbitrate its annotation budget to query languages it is less certain on . |