  Sentence function is an important linguistic feature referring to a user's
purpose in uttering a specific sentence. The use of sentence function has shown
promising results to improve the performance of conversation models. However,
there is no large conversation dataset annotated with sentence functions. In
this work, we collect a new Short-Text Conversation dataset with manually
annotated SEntence FUNctions (STC-Sefun). Classification models are trained on
this dataset to (i) recognize the sentence function of new data in a large
corpus of short-text conversations; (ii) estimate a proper sentence function of
the response given a test query. We later train conversation models conditioned
on the sentence functions, including information retrieval-based and neural
generative models. Experimental results demonstrate that the use of sentence
functions can help improve the quality of the returned responses.
