Learning to select the correct answer in multi-stream question answering

Question answering (QA) is the task of automatically answering a question posed in natural language. Currently, there exists several QA approaches, and, according to recent evaluation results, most of them are complementary. That is, different systems are relevant for different kinds of questions. Somehow, this fact indicates that a pertinent combination of various systems should allow to improve the individual results. This paper focuses on this problem, namely, the selection of the correct answer from a given set of responses corresponding to different QA systems. In particular, it proposes a supervised multi-stream approach that decides about the correctness of answers based on a set of features that describe: (i) the compatibility between question and answer types, (ii) the redundancy of answers across streams, as well as (iii) the overlap and non-overlap information between the question–answer pair and the support text. Experimental results are encouraging; evaluated over a set of 190 questions in Spanish and using answers from 17 different QA systems, our multi-stream QA approach could reach an estimated QA performance of 0.74, significantly outperforming the estimated performance from the best individual system (0.53) as well as the result from best traditional multi-stream QA approach (0.60).