Question Based Importance Weighting Network for Answer Selection

Abstract:

According to the defects of the classic text matching model in the question and answer system, a questionbased importance weighting network for answer selection is proposed. At present, the existing answer selection model generally matches the question sentence and the answer sentence directly, ignoring the influence of noise words in the question sentence and the answer sentence on the match. To solve this problem, the selfattention mechanism is firstly used to modify the weight of each word in the sentence to generate a "clean" question sentence vector. The wordlevel interaction matrix is then used to capture the finegrained semantic information between the question sentence and the answer sentence. It weakens the influence of noise words on the correct answer. Finally, the multiwindow CNN is used to extract the feature information to obtain the prediction result. The comparison experiments on benchmark datasets show that the performance of the BIWN model in the answer selection task is better than the mainstream answer selection algorithm, and the MAP value and MRR value are improved by about 0.7%～6.1%.Finally, the multi-window CNN is used to extract the feature information to obtain the prediction result. The comparison experiments on benchmark datasets show that the performance of the BIWN model in the answer selection task is better than the mainstream answer selection algorithm, and the MAP value and MRR value are improved by about 0.7%-6.1%.