Projects

Current Projects

ANR LawBot

Deep Learning for Judicial Outcome Prediction

2021-2025

Description: LAWBOT is first, an applied research project in law, on the use of automated natural language processing techniques. The LAWBOT project aims to create an artificial case-law intelligence capable of predicting the judicial outcome for a given case, by imitating the decisions previously rendered by the courts on similar cases. LAWBOT is based on an artificial neural network for the deep learning of textual characteristics predictive of the judicial outcome.
Partners: LAMPS Université de Perpignan, LIG Universirté de Grenoble, IRIT Université de Toulouse
IRIS role in the project: Partner

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ANR Guidance

General Purpose Dialogue-assisted Digital Information Access

2023-2027

Description: This project takes place in the context of large language models (LLMs) and conversational systems (e.g. ChatGPT, WebGPT), which have experienced tremendous practical progress in the last few months. The project GUIDANCE aims to conduct research on General Purpose Dialogue-assisted Digital Information Access, specifically how to enable users to access digital information. From a community building perspective, GUIDANCE project aims at federating the Information Retrieval (IR) French Community project, by bringing together experts of the field to advance the development of Dialogue-based Information Access (DbIA) models leveraging LLMs.
Partners: MLIA team, ISIR, Sorbonne Université / CNRS (leader); IRIS and SIG teams, IRIT, Université Toulouse III Paul Sabatier / CNRS; APTIKAL and MRIM teams, LIG, Université Grenoble-Alpes / CNRS; R2I team, LIS, Aix-Marseille Université / CNRS

IRIS role in the project: Partner

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ERC NanoBubbles

Description: The project focuses on how, when and why science fails to correct itself. To understand how the correction of science works or fails, the NanoBubbles project combines approaches from the natural sciences, engineering (natural language processing) and humanities and social sciences (linguistics, sociology, philosophy and history of science).
Partners: University Paris Sorbonne, Maastricht University, University of Grenoble-Alpes, Radboud University, University of Twente, IRIT, Ecole des Ponts
IRIS role in the project: Partner

2020-2026

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ANR MEERQAT

MultimEdia Entity Representation and Question Answering Tasks

2020-2024

Description: We propose to tackle the problem of ambiguities of visual and textual content by learning then combining their representations. As a final use case, we propose to solve a new scientific task, namely Multimedia Question Answering, that requires to rely on three different sources of information to answer a (textual) question with regard to visual data as well as an external knowledge base containing millions of unique entities, each being represented by textual and visual content as well as some links to other entities. In practice, we focus on four types of entities, namely the persons, the organizations, the geographical points of interest and the objects. Achieving such an objective requires to progress on the disambiguation of each modality with respect to the other and the knowledge base. We also propose to merge the representations into a common tri-modal space, in which one should determine the content to associate to an entity to adequately represent it with regard to its type. An important work will deal with the representation of a particular entity into the common space, in which one should determine the content to associate to an entity to adequately represent it. Since such an entity can be associated to several vectors, each corresponding to a data that is originally in a possible different modality, the challenge consists in defining a representation that is quite compact (for performances) while still expressive enough to reflect the potential links of the entity with a variety of other ones. The project has a potential economic impact in the fields of data intelligence, including applications in marketing, security, tourism and cultural heritage. In case of success, the output of the MEERQAT project could directly contribute to improve chatbots. During the project, the direct output will be mainly academic, that scientific article with the corresponding material to reproduce experiments. We also plan to release a new benchmark for the proposed task, in the context of an international evaluation campaign.
Keywords: knowledge base, multimedia content, question answering
Partners: LIST, IRIT, Inria Rennes Bretagne – Atlantique, LIMSI
IRIS role in the project: Partner

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Some Past Projects