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SEarch-oriented ConverSAtional systeMS 

Project SESAM: SEarch-oriented ConverSAtional systeMS 

Until now, in traditional information retrieval (IR) research setting,  the user’s information need is represented by a set of keywords and the returned documents are mainly determined by their inclusion of these keywords.

The project SESAMS envisions a novel paradigm in IR in which the user can interact with the search engine in natural language through the intermediary of a conversational system. We refer to this as search-oriented conversational systems. There are several important challenges underlying this novel paradigm, which we will address in this project:


Conversational IR has a strong relation with general dialogue systems, e.g. chat-bots. In both cases, a multi-turn conversation is made between the user and the system. However, the purpose of conversational IR differs from that of a general chit-chat system: the purpose is to find the desired relevant information more easily in a more natural way, rather than just to keep the conversation going. It is also different from a task-oriented conversation in a closed world because no domain model can be constructed for open-domain IR.

Conversational IR is also related to question-answering (QA). Indeed, IR is generally used as the first step of QA to locate a small set of candidate documents or passages in which answers can be found. Current search engines also include QA as a sub-module, as more and more complete questions are submitted to search engines. However, a big difference between conversational IR and QA is that an information need usually cannot be described by a precise question. The answer to such a query is also not a specific type of entity, but any relevant information. Therefore, conversational IR has to deal with broader user’s demands than QA.


We outline 2 major innovations in this project:

In addition, we address the following challenges:

The results

The expected contribution of this project is twofold, in both deep learning and IR fields: 

Participants will attach particular importance in publishing the proposed contribution in high-venue conferences and journals in both information retrieval (e.g., SIGIR, CIKM, ECIR) and machine learning (e.g., NIPS, ICML, ICLR) communities. We will also participate in workshops dealing with this emerging paradigm (CAIR at SIGIR or SCAI at ICTIR).  All source codes of proposed algorithms will be released to the community in open source.

Partnerships and collaborations

SESAMS is developed at ISIR under the leadership of Laure Soulier who is specialized in Information Retrieval (in particular, interactive IR) and Representation Learning. She will collaborate with specialists with complementary skills:

Project members

Laure Soulier
Maître de conférences HDR
logo ISIR
Vincent Guigue
Professeur affilié