Abstract
We propose an approach to faithfully explaining text classification models, using a specifically designed neural network to
find explanations in the form of machine-annotated rationales
during the prediction process. This results in faithful explanations that are similar to human-annotated rationales, while not
requiring human explanation examples during training. The
quality of found explanations is measured on faithfulness,
quantitative similarity to human explanations, and through a
user evaluation.
find explanations in the form of machine-annotated rationales
during the prediction process. This results in faithful explanations that are similar to human-annotated rationales, while not
requiring human explanation examples during training. The
quality of found explanations is measured on faithfulness,
quantitative similarity to human explanations, and through a
user evaluation.
| Original language | English |
|---|---|
| Number of pages | 8 |
| Publication status | Published - 2021 |
| Event | 35th AAAI Conference on Artificial Intelligence - Duration: 8 Feb 2021 → 9 Feb 2021 |
Conference
| Conference | 35th AAAI Conference on Artificial Intelligence |
|---|---|
| Period | 8/02/21 → 9/02/21 |