Third Recognising Textual Entailment Challenge

The evaluation of all submitted runs will be automatic. The judgments (classifications) returned by the system will be compared to those manually assigned by the human annotators (the Gold Standard). The percentage of matching judgments will provide the accuracy of the run, i.e. the fraction of correct responses.

As a second measure, an Average Precision measure will be computed. This measure evaluates the ability of systems to rank all the T-H pairs in the test set according to their entailment confidence (in decreasing order from the most certain entailment to the least certain). The more the system is confident that T entails H, the higher the ranking is. A perfect ranking would place all the positive pairs (for which the entailment holds) before all the negative pairs. Average precision is a common evaluation measure for system rankings, and is computed as the average of the system's precision values at all points in the ranked list in which recall increases, that is at all points in the ranked list for which the gold standard annotation is YES (Voorhees and Harman, 1999). More formally, it can be written as follows:
1/R * sum for i=1 to n (E(i) *#-correct-up-to-pair-i/i)

where n is the number of the pairs in the test set, R is the total number of positive pairs in the test set, E(i) is 1 if the i-th pair is positive and 0 otherwise, and i ranges over the pairs, ordered by their ranking.

Note the difference between this measure and the Confidence Weighted Score used in the first challenge.

This score will be computed for systems that will provide as output a confidence-ranked list of all test examples (in addition to the YES/NO output for each example).