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2021-12-01 01:29:04 +08:00
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@@ -440,6 +440,15 @@ Bertalign achieves 0.94 F1 score on Text+Berg, which is 4 points higher than the
-g data/text+berg/gold -g data/text+berg/gold
``` ```
```
---------------------------------
| | Strict | Lax |
| Precision | 0.869 | 0.993 |
| Recall | 0.857 | 0.984 |
| F1 | 0.863 | 0.988 |
---------------------------------
```
This F1 score is 4 points lower than that reported in [Thompson & Koehn 2019](https://aclanthology.org/D19-1136/). The original Vecalign paper uses [LASER](https://github.com/facebookresearch/LASER) to embed the source and target texts while we use [sentence-transformers](https://github.com/UKPLab/sentence-transformers), which may have caused the gap. This F1 score is 4 points lower than that reported in [Thompson & Koehn 2019](https://aclanthology.org/D19-1136/). The original Vecalign paper uses [LASER](https://github.com/facebookresearch/LASER) to embed the source and target texts while we use [sentence-transformers](https://github.com/UKPLab/sentence-transformers), which may have caused the gap.
## Post-processing ## Post-processing