  This paper presents our work for the ninth edition of the Dialogue System
Technology Challenge (DSTC9). Our solution addresses the track number four:
Simulated Interactive MultiModal Conversations. The task consists in providing
an algorithm able to simulate a shopping assistant that supports the user with
his/her requests. We address the task of response retrieval, that is the task
of retrieving the most appropriate agent response from a pool of response
candidates. Our approach makes use of a neural architecture based on
transformer with a multi-attentive structure that conditions the response of
the agent on the request made by the user and on the product the user is
referring to. Final experiments on the SIMMC Fashion Dataset show that our
approach achieves the second best scores on all the retrieval metrics defined
by the organizers. The source code is available at
https://github.com/D2KLab/dstc9-SIMMC.
