  Open-domain conversational systems are assumed to generate equally good
responses on multiple domains. Previous work achieved good performance on the
single corpus, but training and evaluating on multiple corpora from different
domains are less studied. This paper explores methods of generating relevant
responses for each of multiple multi-domain corpora. We first examine
interleaved learning which intermingles multiple corpora as the baseline. We
then investigate two multi-domain learning methods, labeled learning and
multi-task labeled learning, which encode each corpus through a unique corpus
embedding. Furthermore, we propose Domain-specific Frequency (DF), a novel
word-level importance weight that measures the relative importance of a word
for a specific corpus compared to other corpora. Based on DF, we propose
weighted learning, a method that integrates DF to the loss function. We also
adopt DF as a new evaluation metric. Extensive experiments show that our
methods gain significant improvements on both automatic and human evaluation.
We share our code and data for reproducibility

Computation and Language