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A number of learned sparse and dense retrieval approaches have recently beenproposed and proven effective in tasks such as passage retrieval and documentretrieval. In this paper we analyze with a replicability study if the lessonslearned generalize to the retrieval of responses for dialogues, an importanttask for the increasingly popular field of conversational search. Unlikepassage and document retrieval where documents are usually longer than queries,in response ranking for dialogues the queries (dialogue contexts) are oftenlonger than the documents (responses). Additionally, dialogues have aparticular structure, i.e. multiple utterances by different users. With thesedifferences in mind, we here evaluate how generalizable the following majorfindings from previous works are: (F1) query expansion outperforms ano-expansion baseline; (F2) document expansion outperforms a no-expansionbaseline; (F3) zero-shot dense retrieval underperforms sparse baselines; (F4)dense retrieval outperforms sparse baselines; (F5) hard negative sampling isbetter than random sampling for training dense models. Our experiments -- basedon three different information-seeking dialogue datasets -- reveal that fourout of five findings (F2-F5) generalize to our domain

Do the Findings of Document and Passage Retrieval Generalize to the Retrieval of Responses for Dialogues?