A study on output sentence generation method for question answering using statistical machine translation

Question answering generally generates the answer to the question by extracting the named entity from the sentences containing the answer to the question from information sources. However, it is not always true that a named entity is an answer to the question. So we propose a method for generating the answer sentence using statistical machine translation. The probability models are constructed by learning from enormous samples of the set of question sentence, extracted sentence, and answer sentence. The question sentence and the sentence extracted by the question answering from information source are regarded as an input of machine translation. They are translated to a suitable answer sentence to the question. In this paper, we attempted to apply our method to several simple types questions that can also be answered by the named entity extraction.