University of Hagen at QA@CLEF 2008: Answer Validation Exercise
Ingo Gl · 2008
RAVE (Real-time Answer Validation Engine) is a logic-based answer validator/selector designed for application in real-time question answering. RAVE uses the same toolchain for deep linguistic analysis and the same background knowledge as its predecessor (MAVE), which took part in the AVE 2007. However, a full logical answer check as in MAVE was not considered suitable for real-time answer validation since it requires parsing of all answer candidates. Therefore RAVE uses a simplified validation model where the prover only checks if the support passage contains a correct answer at all. This move from logic-based answer validation to logical validation of supporting snippets permits RAVE to avoid any parsing of answers, i.e. the system only needs a parse of the question and pre-computed snippet analyses. In this way very low validation/selection times can be achieved. Machine learning is used for assigning local validation scores using both logic-based and shallow features. The resulting local validation scores are improved by aggregation. One of the key features of RAVE is its innovative aggregation model, which is robust against duplicated information in the support passages. In this model, the effect of aggregation is controlled by the lexical diversity of the support passages for a given answer. If the support passages have no terms in common, then the aggregation has maximal effect and the passages are treated as providing independent evidence. Repetition of a support passage, by contrast, has no effect on the results of aggregation at all. In order to obtain a richer basis for aggregation, an active validation approach was chosen, i.e. the original pool of support passages in the AVE 2008 test set was enhanced by retrieving additional support passages from the CLEF corpora. This technique already proved effective in the AVE 2007. The development of RAVE is not finished yet, but the system already achieved an F-score of 0.39 and a selection rate of 0.61 compared to optimal selection. Judging from last year’s runs of MAVE (with a 0.93 selection rate and F-score of 0.72), this may look disappointing. However, the AVE task for German was much more difficult this year, and the F-score gain of RAVE (over the 100% yes baseline) and qa-accuracy gain (compared to random selection) are better than in last year’s runs of MAVE. 1