In this paper, we present a comment labeling system based on a deep learning strategy. We treat the answer selection task as a sequence labeling problem and propose recurrent convolution neural networks to recognize good comments. In the recurrent architecture of our system, our approach uses 2-dimensional convolutional neural networks to learn the distributed representation for question-commen...

Question Answering has come a long way from answer sentence selection, relational QA to reading and comprehension. We move our attention to abstractive question answering by which we facilitate machine to read passages and answer questions by generating them. We frame the problem as a sequence to sequence learning where the encoder being a network that models the relation between question and p...

Generating answer with natural language sentence is very important in real-world question answering systems, which needs to obtain a right answer as well as a coherent natural response. In this paper, we propose an end-to-end question answering system called COREQA in sequence-to-sequence learning, which incorporates copying and retrieving mechanisms to generate natural answers within an encode...

In this paper we answer the following question: what is the lexicographically least sequence over the natural numbers that avoids 32 -powers?