Papers by Martina Forster
Determinantal Beam Search (2021.acl-long)
Copied to clipboard
| Challenge: | a new beam search approach allows for a diverse subset selection process . standard beam search does not encode interactions between candidates . |
| Approach: | They propose a beam search reformulation that casts subset selection as the subdeterminant optimization problem. |
| Outcome: | The proposed method offers competitive performance against other diverse set generation strategies while providing a more general approach to optimizing for diversity. |
Searching for Search Errors in Neural Morphological Inflection (2021.eacl-main)
Copied to clipboard
| Challenge: | Neural sequence-to-sequence models are the predominant choice for language generation tasks. |
| Approach: | They find that on word-level tasks, the empty string is often the global optimum . they suggest that the poor calibration of many neural models may stem from characteristics of a specific subset of tasks rather than general ill-suitedness of such models for language generation. |
| Outcome: | The results suggest that the poor calibration of many neural models may stem from characteristics of a specific subset of tasks rather than general ill-suitedness of such models for language generation. |