Abstract
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). Classifi-
cation 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