  The use of conversational assistants to search for information is becoming
increasingly more popular among the general public, pushing the research
towards more advanced and sophisticated techniques. In the last few years, in
particular, the interest in conversational search is increasing, not only
because of the generalization of conversational assistants but also because
conversational search is a step forward in allowing a more natural interaction
with the system.
  In this work, the focus is on exploring the context present of the
conversation via the historical utterances and respective embeddings with the
aim of developing a conversational search system that helps people search for
information in a natural way. In particular, this system must be able to
understand the context where the question is posed, tracking the current state
of the conversation and detecting mentions to previous questions and answers.
We achieve this by using a context-tracking component based on neural
query-rewriting models. Another crucial aspect of the system is to provide the
most relevant answers given the question and the conversational history. To
achieve this objective, we used a Transformer-based re-ranking method and
expanded this architecture to use the conversational context.
  The results obtained with the system developed showed the advantages of using
the context present in the natural language utterances and in the neural
embeddings generated throughout the conversation.
