Leaderboard

RankTeam NameMAPAvgRECMRRPrecisionRecallF1Accuracy
1unocanda0.75360.899679.88890.42801.00000.59940.4280
2outdex0.74760.908982.00000.71760.85510.78040.7940
3a0010.74600.896680.33330.42801.00000.59940.4280
4sakuraSAT0.74600.896680.33330.80340.66820.72960.7880
5nakedGun0.74580.899081.06670.82800.60750.70080.7780
6Ninofiero0.74420.896580.33330.42801.00000.59940.4280
7MI0.74300.899281.06670.92980.24770.39110.6700
8SDGMAX0.73940.896879.56670.42801.00000.59940.4280
9jteamNLU0.73830.893380.28570.42801.00000.59940.4280
10ph01230.73810.893480.40000.42801.00000.59940.4280
11lazyant0.73510.890880.25000.42801.00000.59940.4280
12SlogSweep0.73460.895477.50000.42801.00000.59940.4280
13NinjaTurtles0.73210.898580.16670.00000.00000.00000.5720
14HappyHippo0.72830.889278.50000.42801.00000.59940.4280
15RushGW0.72750.882779.50000.76840.68220.72280.7760
16BayanLTR0.72650.891178.16670.42801.00000.59940.4280
17NxGTR0.72380.890779.56670.78820.74770.76740.8060
18Ensemble0.72320.887080.40000.76530.76170.76350.7980
19PadovaComanda0.71490.881078.40000.42801.00000.59940.4280
20CrVaDr0.70960.887875.90000.42801.00000.59940.4280
21LSI0.52830.708362.41110.40860.49070.44590.4780
Baseline 0.7392 0.9056 80.83 0.8239 0.6776 0.7436 0.80
The performance values refer to the Development set.
The leaderboard will be updated periodically, so don't worry if your best result does not appear on the leaderboard right away.
We recall that the official evaluation measure for the competition is mean average precision (MAP).

cQA Challenge Chairs

  • Alberto Barrón-Cedeño, Qatar Computing Research Institute
  • Giovanni Da San Martino, Qatar Computing Research Institute
  • Simone Filice, Università degli Studi di Roma "Tor Vergata"
  • Preslav Nakov, Qatar Computing Research Institute

Discovery Challenge Chairs

  • Elio Masciari, ICAR CNR, Italy
  • Alessandro Moschitti, University of Trento, Italy

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