  Despite the effectiveness of sequence-to-sequence framework on the task of
Short-Text Conversation (STC), the issue of under-exploitation of training data
(i.e., the supervision signals from query text is \textit{ignored}) still
remains unresolved. Also, the adopted \textit{maximization}-based decoding
strategies, inclined to generating the generic responses or responses with
repetition, are unsuited to the STC task. In this paper, we propose to
formulate the STC task as a language modeling problem and tailor-make a
training strategy to adapt a language model for response generation. To enhance
generation performance, we design a relevance-promoting transformer language
model, which performs additional supervised source attention after the
self-attention to increase the importance of informative query tokens in
calculating the token-level representation. The model further refines the query
representation with relevance clues inferred from its multiple references
during training. In testing, we adopt a
\textit{randomization-over-maximization} strategy to reduce the generation of
generic responses. Experimental results on a large Chinese STC dataset
demonstrate the superiority of the proposed model on relevance metrics and
diversity metrics.\footnote{Code available at
https://ai.tencent.com/ailab/nlp/dialogue/.
