Towards Query Performance Prediction for Neural Information Retrieval: Challenges and Opportunities

Author:

Faggioli Guglielmo1ORCID,Formal Thibault2ORCID,Lupart Simon2ORCID,Marchesin Stefano1ORCID,Clinchant Stephane2ORCID,Ferro Nicola1ORCID,Piwowarski Benjamin3ORCID

Affiliation:

1. University of Padova, Padova, Italy

2. Naver Labs Europe, Meylan, France

3. CNRS / ISIR, Sorbonne Université, Paris, France

Publisher

ACM

Reference114 articles.

1. BERT-QPP: Contextualized Pre-trained transformers for Query Performance Prediction

2. Unsupervised Question Clarity Prediction through Retrieved Item Coherency

3. Neural embedding-based specificity metrics for pre-retrieval query performance prediction

4. Negar Arabzadeh , Fattane Zarrinkalam , Jelena Jovanovic , and Ebrahim Bagheri . 2020 b. Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction. In Advances in Information Retrieval - 42nd European Conference on IR Research , ECIR 2020, Lisbon, Portugal, April 14--17, 2020, Proceedings, Part II (Lecture Notes in Computer Science , Vol. 12036), Joemon M. Jose, Emine Yilmaz, Jo a o Magalh a es, Pablo Castells, Nicola Ferro, Má rio J. Silva, and Flá vio Martins (Eds.). Springer, 78-- 85 . https://doi.org/10.1007/978--3-030--45442--5_10 10.1007/978--3-030--45442--5_10 Negar Arabzadeh, Fattane Zarrinkalam, Jelena Jovanovic, and Ebrahim Bagheri. 2020b. Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction. In Advances in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14--17, 2020, Proceedings, Part II (Lecture Notes in Computer Science, Vol. 12036), Joemon M. Jose, Emine Yilmaz, Jo a o Magalh a es, Pablo Castells, Nicola Ferro, Má rio J. Silva, and Flá vio Martins (Eds.). Springer, 78--85. https://doi.org/10.1007/978--3-030--45442--5_10

5. Yang Bai Xiaoguang Li Gang Wang Chaoliang Zhang Lifeng Shang Jun Xu Zhaowei Wang Fangshan Wang and Qun Liu. 2020. SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval. https://doi.org/10.48550/ARXIV.2010.00768 10.48550/ARXIV.2010.00768

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