  A number of learned sparse and dense retrieval approaches have recently been
proposed and proven effective in tasks such as passage retrieval and document
retrieval. In this paper we analyze with a replicability study if the lessons
learned generalize to the retrieval of responses for dialogues, an important
task for the increasingly popular field of conversational search. Unlike
passage and document retrieval where documents are usually longer than queries,
in response ranking for dialogues the queries (dialogue contexts) are often
longer than the documents (responses). Additionally, dialogues have a
particular structure, i.e. multiple utterances by different users. With these
differences in mind, we here evaluate how generalizable the following major
findings from previous works are: (F1) query expansion outperforms a
no-expansion baseline; (F2) document expansion outperforms a no-expansion
baseline; (F3) zero-shot dense retrieval underperforms sparse baselines; (F4)
dense retrieval outperforms sparse baselines; (F5) hard negative sampling is
better than random sampling for training dense models. Our experiments -- based
on three different information-seeking dialogue datasets -- reveal that four
out of five findings (F2-F5) generalize to our domain
