  Recent works using artificial neural networks based on word distributed
representation greatly boost the performance of various natural language
learning tasks, especially question answering. Though, they also carry along
with some attendant problems, such as corpus selection for embedding learning,
dictionary transformation for different learning tasks, etc. In this paper, we
propose to straightforwardly model sentences by means of character sequences,
and then utilize convolutional neural networks to integrate character embedding
learning together with point-wise answer selection training. Compared with deep
models pre-trained on word embedding (WE) strategy, our character-sequential
representation (CSR) based method shows a much simpler procedure and more
stable performance across different benchmarks. Extensive experiments on two
benchmark answer selection datasets exhibit the competitive performance
compared with the state-of-the-art methods.

Computation and Language