Abstract.One major problem of state-of-the-art Cross Language Question Answering systems is the translation of user questions. This paper proposes combining the potential of multiple translation machines in order to improve the final answering precision. In particular, it presents three different methods for this purpose. The first one focuses on selecting the most fluent translation from a given set; the second one combines the passages recovered by several question translations; finally, the third one constructs a new question reformulation by merging word sequences from different translations. Experimental results demonstrated that the proposed approaches allow reducing the error rates in relation to a monolingual question answering exercise.
This paper explores the feasibility of a multilingual question answering approach based on the Web redundancy. The paper introduces a system prototype that combines a translation machine with a statistical QA method. The main advantage of this proposal is its small dependence to a given language. The experimental results demonstrated the great potential of the approach and gave interesting insights about the Web redundancy and the online translators.
Abstract. One major problem in multilingual Question Answering (QA) is the combination of answers obtained from different languages into one single ranked list. This paper proposes a new method for tackling this problem. This method is founded on a graph-based ranking approach inspired in the popular Google's PageRank algorithm. Experimental results demonstrate that the proposed method outperforms other current techniques for answer fusion, and also evidence the advantages of multilingual QA over the traditional monolingual approach.
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