Model and performance details for studies testing on high-expertise and non-narrative domains. Fine method categories used in these studies include feature augmentation (FA), loss augmentation (LA), ensembling (EN), pretraining (PT), parameter initialization (PI), and pseudo-labeling (PL).
| Study . | Method . | Performance . |
|---|---|---|
| (Arnold et al., 2008) | Manually constructed feature hierarchy across domains, allowing back off to more general features (FA) | Positive transfer from 5 corpora (biomedical, news, email) to email |
| (McClosky et al., 2010) | Mixture of domain-specific models chosen via source-target similarity features (e.g., cosine similarity) (EN) | Positive transfer to biomedical, literature and conversation domains |
| (Yang and Eisenstein, 2015) | Dense embeddings induced from template features and manually defined domain attribute embeddings (FA) | Positive transfer to 4/5 web domains and 10/11 literary periods |
| (Xing et al., 2018) | Multi-task learning method with source-target distance minimization as additional loss term (LA) | Positive transfer on 4/6 intra-medical settings (EHRs, forums) and 5/9 narrative to medical settings |
| (Wang et al., 2018) | Source-target distance minimized using two loss penalties (LA) | Positive transfer to medical and Twitter data |
| Study . | Method . | Performance . |
|---|---|---|
| (Arnold et al., 2008) | Manually constructed feature hierarchy across domains, allowing back off to more general features (FA) | Positive transfer from 5 corpora (biomedical, news, email) to email |
| (McClosky et al., 2010) | Mixture of domain-specific models chosen via source-target similarity features (e.g., cosine similarity) (EN) | Positive transfer to biomedical, literature and conversation domains |
| (Yang and Eisenstein, 2015) | Dense embeddings induced from template features and manually defined domain attribute embeddings (FA) | Positive transfer to 4/5 web domains and 10/11 literary periods |
| (Xing et al., 2018) | Multi-task learning method with source-target distance minimization as additional loss term (LA) | Positive transfer on 4/6 intra-medical settings (EHRs, forums) and 5/9 narrative to medical settings |
| (Wang et al., 2018) | Source-target distance minimized using two loss penalties (LA) | Positive transfer to medical and Twitter data |