  Human conversations contain many types of information, e.g., knowledge,
common sense, and language habits. In this paper, we propose a conversational
word embedding method named PR-Embedding, which utilizes the conversation pairs
$ \left\langle{post, reply} \right\rangle$ to learn word embedding. Different
from previous works, PR-Embedding uses the vectors from two different semantic
spaces to represent the words in post and reply. To catch the information among
the pair, we first introduce the word alignment model from statistical machine
translation to generate the cross-sentence window, then train the embedding on
word-level and sentence-level. We evaluate the method on single-turn and
multi-turn response selection tasks for retrieval-based dialog systems. The
experiment results show that PR-Embedding can improve the quality of the
selected response. PR-Embedding source code is available at
https://github.com/wtma/PR-Embedding
