PHD position at IRIT (Toulouse) funded by the ANR COST project (2019-2022)
Title: Deep models for task-based information retrieval
Keywords: information retrieval, deep sequential models, deep
reinforcement learning
Advisors: Eric Gaussier (eric.gaussier@imag.fr), Karen Pinel-Sauvagnat
(karen.pinel-sauvagant@irit.fr), Lynda Tamine-Lechani
(lynda.lechani@irit.fr)
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Context
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While search systems today are very effective for simple look-up
information tasks (fact-finding search), they are unable to guide users
engaged in exploratory, multi-step and highly cognitive search tasks
(e.g, diagnosis, human learning). Hence, paradoxically, while we
consider information search nowadays to be ’natural’ and ’easy’, search
systems are not yet able to provide adequate support for achieving a
wide range of real-life work complex search tasks[1]. In the CoST
project https://www.irit.fr/COST/ (funded by ANR 2019-2022), we envision
a shift from search engines to task completion engines by dynamically
assisting users in making the optimal decisions, empowering them to
achieve multi-step complex search tasks. While most of previous work
rely on query-aware models and techniques to structure the session
context and model search satisfaction [2,3,4] at the query level, we
rather attempt to design task-aware IR models to make task-level
satisfaction predictions.
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PhD
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This PhD will be focussed on applying neural approaches for task-based
information retrieval. Based on the findings that have raised from
previous works about the effectiveness of seq2seg models to capture
reformulation patterns for the next query prediction task [4,5], we
envision new end-to-end network architectures that make possible to
account for sequences of sub-tasks. We will also explore end-to-end
learning for task satisfaction prediction based on deep reinforcement
learning that goes beyond query-level relevance. The candidate will
investigate the modelling, the deployment and evaluation of search
assistance techniques (eg., query suggestion) and ranking models using
deep neural networks architectures. The evaluation of the resulting
systems will be carried out using both public benchmarks (eg., TREC
Tasks, TREC session, AOL dataset) as well as laboratory-built datasets
built within the CoST project.
– Starting and duration: September 2019, 36 months
– Skills: Background in information retrieval and machine learning would
be greatly appreciated but not mandatory
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Application process: Deadline March, 30th 2019.
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To apply, please email your application to: eric.gaussier@imag.fr,
karen.sauvagnat@irit.fr, lynda.lechani@irit.fr.
The application should consist of the following:
+ a curriculum vitae
+ transcript of marks according to M1-M2 profile or last 3 years of
engineering school (with indication on the ranking if possible)
+ covering letter
+ letter(s) of recommendation including at least one letter drawn up by
a university referent
Potential candidates will be invited for an interview with the supervisors.
[1]Ahmed Hassan Awadallah, Ryen W. White, Patrick Pantel, Susan T.Dumais, and Yi-Min Wang. Supporting Complex Search Tasks, CIKM’2014.
[2] Jiyun Luo, Sicong Zhang, and Hui Yang. 2014. Win-win search:
dual-agent stochastic game in session search, SIGIR’2014
[3] Bhaskar Mitra. 2015. Exploring Session Context using Distributed
Representations of Queries and Reformulations, SIGIR’2015
[4] Mostafa Dehghani, Sascha Rothe, Enrique Alfonseca, and Pascal
Fleury. Learning to Attend, Copy, and Generate for Session-Based Query
Suggestion, CIKM’2017
[5] Alessandro Sordoni, Yoshua Bengio, Hossein Vahabi, Christina Lioma,
Jakob Grue Simonsen, and Jian-Yun Nie. A Hierarchical Recurrent
Encoder-Decoder for Generative Context-Aware Query Suggestion, CIKM’2015