Representation Learning for Answer Selection with LSTM-Based Importance Weighting


We present an approach to non-factoid answer selection with a separate component based on BiLSTM to determine the importance of segments in the input. In contrast to other recently proposed attention-based models within the same area, we determine the importance while assuming the independence of questions and candidate answers. Experimental results show… (More)


8 Figures and Tables