  People often answer yes-no questions without explicitly saying yes, no, or
similar polar keywords. Figuring out the meaning of indirect answers is
challenging, even for large language models. In this paper, we investigate this
problem working with dialogues from multiple domains. We present new benchmarks
in three diverse domains: movie scripts, tennis interviews, and airline
customer service. We present an approach grounded on distant supervision and
blended training to quickly adapt to a new dialogue domain. Experimental
results show that our approach is never detrimental and yields F1 improvements
as high as 11-34%.
