Lynda Tamine
2018-12-10 18:08:11 UTC
PHD and Internship positions 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 (***@imag.fr), Karen Pinel-Sauvagnat (karen.pinel-***@irit.fr), Lynda Tamine-Lechani (***@irit.fr)
===============
Context
===============
While search systems today are very efficient 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 (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: the successful candidate is expected to have skills/background in information retrieval, machine learning, deep learning. Background in reinforcement learning would be greatly appreciated.
===================================
Internship.
===================================
This internship will be focused on: (1) a review of recent neural approaches for next query prediction in session-based search; (2) the development of a baseline framework for query prediction in task-based search.
- Starting and duration: March 2019, 4-6 months
- The successful candidate is expected to have skills/background in information retrieval and machine learning.
===================================
Application process: Deadline March, 30th 2019.
===================================
To apply, please email your application to: ***@imag.fr, ***@irit.fr, ***@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
_______________________________________________
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Title: Deep models for task-based information retrieval
Keywords: information retrieval, deep sequential models, deep reinforcement learning
Advisors: Eric Gaussier (***@imag.fr), Karen Pinel-Sauvagnat (karen.pinel-***@irit.fr), Lynda Tamine-Lechani (***@irit.fr)
===============
Context
===============
While search systems today are very efficient 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 (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.
===================================
PhD
===================================
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: the successful candidate is expected to have skills/background in information retrieval, machine learning, deep learning. Background in reinforcement learning would be greatly appreciated.
===================================
Internship.
===================================
This internship will be focused on: (1) a review of recent neural approaches for next query prediction in session-based search; (2) the development of a baseline framework for query prediction in task-based search.
- Starting and duration: March 2019, 4-6 months
- The successful candidate is expected to have skills/background in information retrieval and machine learning.
===================================
Application process: Deadline March, 30th 2019.
===================================
To apply, please email your application to: ***@imag.fr, ***@irit.fr, ***@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
_______________________________________________
Please do not post msgs that are not relevant to the database community at large. Go to www.cs.wisc.edu/dbworld for guidelines and posting forms.
To unsubscribe, go to https://lists.cs.wisc.edu/mailman/listinfo/dbworld