  In a conversational context, a user expresses her multi-faceted information
need as a sequence of natural-language questions, i.e., utterances. Starting
from a given topic, the conversation evolves through user utterances and system
replies. The retrieval of documents relevant to a given utterance in a
conversation is challenging due to ambiguity of natural language and to the
difficulty of detecting possible topic shifts and semantic relationships among
utterances. We adopt the 2019 TREC Conversational Assistant Track (CAsT)
framework to experiment with a modular architecture performing: (i) topic-aware
utterance rewriting, (ii) retrieval of candidate passages for the rewritten
utterances, and (iii) neural-based re-ranking of candidate passages. We present
a comprehensive experimental evaluation of the architecture assessed in terms
of traditional IR metrics at small cutoffs. Experimental results show the
effectiveness of our techniques that achieve an improvement up to 0.28 (+93%)
for P@1 and 0.19 (+89.9%) for nDCG@3 w.r.t. the CAsT baseline.
