Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2306.15261 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912069617451008 |
|---|---|
| author | Li, Xinzhe Liu, Ming Gao, Shang Buntine, Wray |
| author_facet | Li, Xinzhe Liu, Ming Gao, Shang Buntine, Wray |
| contents | Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models. However, a comprehensive, integrated discussion of the three research lines is still lacking in the literature. In this survey, we 1) compare the three lines of research under a unifying definition; 2) summarize the data-generating processes and evaluation protocols for each line of research; and 3) emphasize the challenges and opportunities for future work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_15261 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Survey on Out-of-Distribution Evaluation of Neural NLP Models Li, Xinzhe Liu, Ming Gao, Shang Buntine, Wray Computation and Language Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models. However, a comprehensive, integrated discussion of the three research lines is still lacking in the literature. In this survey, we 1) compare the three lines of research under a unifying definition; 2) summarize the data-generating processes and evaluation protocols for each line of research; and 3) emphasize the challenges and opportunities for future work. |
| title | A Survey on Out-of-Distribution Evaluation of Neural NLP Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2306.15261 |