Search Results for author: Laure Soulier

Found 29 papers, 13 papers with code

Simple Domain Adaptation for Sparse Retrievers

no code implementations21 Jan 2024 Mathias Vast, Yuxuan Zong, Basile Van Cooten, Benjamin Piwowarski, Laure Soulier

In Information Retrieval, and more generally in Natural Language Processing, adapting models to specific domains is conducted through fine-tuning.

Domain Adaptation Information Retrieval +1

Augmenting Ad-Hoc IR Dataset for Interactive Conversational Search

no code implementations10 Nov 2023 Pierre Erbacher, Jian-Yun Nie, Philippe Preux, Laure Soulier

The only two datasets known to us that contain both document relevance judgments and the associated clarification interactions are Qulac and ClariQ.

Conversational Search

CIRCLE: Multi-Turn Query Clarifications with Reinforcement Learning

no code implementations5 Nov 2023 Pierre Erbacher, Laure Soulier

In this paper, we introduce CIRCLE, a generative model for multi-turn query Clarifications wIth ReinforCement LEarning that leverages multi-turn interactions through a user simulation framework.

Language Modelling reinforcement-learning +1

Improving generalization in large language models by learning prefix subspaces

no code implementations24 Oct 2023 Louis Falissard, Vincent Guigue, Laure Soulier

We show in this paper that "Parameter Efficient Fine-Tuning" (PEFT) methods, however, are perfectly compatible with this original approach, and propose to learn entire simplex of continuous prefixes.

Few-Shot Learning

Dynamic Named Entity Recognition

1 code implementation16 Feb 2023 Tristan Luiggi, Laure Soulier, Vincent Guigue, Siwar Jendoubi, Aurélien Baelde

Named Entity Recognition (NER) is a challenging and widely studied task that involves detecting and typing entities in text.

Entity Typing Memorization +3

CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval

no code implementations11 Jan 2023 Nam Le Hai, Thomas Gerald, Thibault Formal, Jian-Yun Nie, Benjamin Piwowarski, Laure Soulier

Conversational search is a difficult task as it aims at retrieving documents based not only on the current user query but also on the full conversation history.

Conversational Search Information Retrieval +2

Building a Subspace of Policies for Scalable Continual Learning

1 code implementation18 Nov 2022 Jean-Baptiste Gaya, Thang Doan, Lucas Caccia, Laure Soulier, Ludovic Denoyer, Roberta Raileanu

We introduce Continual Subspace of Policies (CSP), a new approach that incrementally builds a subspace of policies for training a reinforcement learning agent on a sequence of tasks.

Continual Learning

Interactive Query Clarification and Refinement via User Simulation

no code implementations31 May 2022 Pierre Erbacher, Ludovic Denoyer, Laure Soulier

When users initiate search sessions, their queries are often unclear or might lack of context; this resulting in inefficient document ranking.

Document Ranking Information Retrieval +2

Continual Learning of Long Topic Sequences in Neural Information Retrieval

1 code implementation10 Jan 2022 Thomas Gerald, Laure Soulier

In information retrieval (IR) systems, trends and users' interests may change over time, altering either the distribution of requests or contents to be recommended.

Continual Learning Information Retrieval +1

State of the Art of User Simulation approaches for conversational information retrieval

no code implementations10 Jan 2022 Pierre Erbacher, Laure Soulier, Ludovic Denoyer

Conversational Information Retrieval (CIR) is an emerging field of Information Retrieval (IR) at the intersection of interactive IR and dialogue systems for open domain information needs.

Decision Making Information Retrieval +4

Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation

2 code implementations EMNLP 2021 Clément Rebuffel, Thomas Scialom, Laure Soulier, Benjamin Piwowarski, Sylvain Lamprier, Jacopo Staiano, Geoffrey Scoutheeten, Patrick Gallinari

QuestEval is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions.

Data-to-Text Generation Question Generation +1

Incorporating Visual Semantics into Sentence Representations within a Grounded Space

no code implementations IJCNLP 2019 Patrick Bordes, Eloi Zablocki, Laure Soulier, Benjamin Piwowarski, Patrick Gallinari

To overcome this limitation, we propose to transfer visual information to textual representations by learning an intermediate representation space: the grounded space.


Conversational Search for Learning Technologies

no code implementations9 Jan 2020 Sharon Oviatt, Laure Soulier

Conversational search is based on a user-system cooperation with the objective to solve an information-seeking task.

Conversational Search Information Retrieval +1

Context-Aware Zero-Shot Learning for Object Recognition

no code implementations24 Apr 2019 Eloi Zablocki, Patrick Bordes, Benjamin Piwowarski, Laure Soulier, Patrick Gallinari

Zero-Shot Learning (ZSL) aims at classifying unlabeled objects by leveraging auxiliary knowledge, such as semantic representations.

Object Object Recognition +1

Images & Recipes: Retrieval in the cooking context

1 code implementation2 May 2018 Micael Carvalho, Rémi Cadène, David Picard, Laure Soulier, Matthieu Cord

Recent advances in the machine learning community allowed different use cases to emerge, as its association to domains like cooking which created the computational cuisine.

BIG-bench Machine Learning Retrieval

Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings

1 code implementation30 Apr 2018 Micael Carvalho, Rémi Cadène, David Picard, Laure Soulier, Nicolas Thome, Matthieu Cord

Designing powerful tools that support cooking activities has rapidly gained popularity due to the massive amounts of available data, as well as recent advances in machine learning that are capable of analyzing them.

BIG-bench Machine Learning Cross-Modal Retrieval +1

Learning Multi-Modal Word Representation Grounded in Visual Context

no code implementations9 Nov 2017 Éloi Zablocki, Benjamin Piwowarski, Laure Soulier, Patrick Gallinari

Representing the semantics of words is a long-standing problem for the natural language processing community.

Word Embeddings

DSRIM: A Deep Neural Information Retrieval Model Enhanced by a Knowledge Resource Driven Representation of Documents

no code implementations15 Jun 2017 Gia-Hung Nguyen, Laure Soulier, Lynda Tamine, Nathalie Bricon-Souf

The state-of-the-art solutions to the vocabulary mismatch in information retrieval (IR) mainly aim at leveraging either the relational semantics provided by external resources or the distributional semantics, recently investigated by deep neural approaches.

Information Retrieval Retrieval

Toward a Deep Neural Approach for Knowledge-Based IR

no code implementations23 Jun 2016 Gia-Hung Nguyen, Lynda Tamine, Laure Soulier, Nathalie Bricon-Souf

With this in mind, we argue that embedding KBs within deep neural architectures supporting documentquery matching would give rise to fine-grained latent representations of both words and their semantic relations.

Document Ranking Implicit Relations +2

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