  Quotations are crucial for successful explanations and persuasions in
interpersonal communications. However, finding what to quote in a conversation
is challenging for both humans and machines. This work studies automatic
quotation generation in an online conversation and explores how language
consistency affects whether a quotation fits the given context. Here, we
capture the contextual consistency of a quotation in terms of latent topics,
interactions with the dialogue history, and coherence to the query turn's
existing content. Further, an encoder-decoder neural framework is employed to
continue the context with a quotation via language generation. Experiment
results on two large-scale datasets in English and Chinese demonstrate that our
quotation generation model outperforms the state-of-the-art models. Further
analysis shows that topic, interaction, and query consistency are all helpful
to learn how to quote in online conversations.
