Comments (2)
Metric | NLTK | DRQA Sents Precomputed IDF | DRQA Sents New IDF |
---|---|---|---|
Runtime | 2 hours | 10 hours | 12 hours |
Strict Accuracy (strict) requirement for correct evidence | 0.2476 | 0.1827 | 0.2698 |
Classification Accuracy Without Need For Evidence | 0.4885 | 0.4588 | 0.4922 |
Correct Document Return Rate (dmatch) | 0.5793 | 0.5893 | 0.5893 |
Correct Document Return Rate after sentence selection (smatch) | 0.4773 | 0.2690 | 0.5596 |
Correct Text Return Rate (for Refutes/Supports) | 0.3647 | 0.1083 | 0.4680 |
from naacl2018-fever.
@andreasvlachos using DrQA instead of NLTK for sentence selection gives us a 2% boost - at the cost of an extra 10 hours. dmatch and smatch figures give us upper bounds for strict accuracy (considering the supported/refuted class). In the case of DrQA - the number of times the correct document is in the evidence after sentence selection is 55% of the time whereas using NLTK, this is only 47%.
from naacl2018-fever.
Related Issues (20)
- MLP model training crashes if models directory doesnt exist
- problems with model 2 from readme HOT 2
- Some training data files missing HOT 6
- Cannot find "OnlineTfidfDocRanker" HOT 5
- MLP training will crash if models directory doesn't exist
- ImportError: cannot import name 'Dataset' HOT 3
- Failed to build DrQA
- NameError: name 'get_count_matrix' is not defined HOT 2
- Key Error when running drQA HOT 1
- installation fails with pip 10.0.1 HOT 2
- Got TypeError: unhashable type: 'list' when running eval_mrr.py
- Evidence Retrieval Evaluation being killed
- Evaluation speed HOT 1
- Get error in the initialization regex HOT 1
- pytorch 0.3.1 seems too outdated to install HOT 1
- fever competition
- Pre-trained model.tar.gz Not Available HOT 2
- Error in Data Preparation HOT 2
- Can't download dataset HOT 2
- Can't download pretrained model HOT 3
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from naacl2018-fever.