Hofstatter, S;
Lipani, A;
Althammer, S;
Zlabinger, M;
Hanbury, A;
(2021)
Mitigating the Position Bias of Transformer Models in Passage Re-Ranking.
In:
Advances in Information Retrieval. ECIR 2021.
(pp. pp. 238-253).
Springer, Cham
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Abstract
Supervised machine learning models and their evaluation strongly depends on the quality of the underlying dataset. When we search for a relevant piece of information it may appear anywhere in a given passage. However, we observe a bias in the position of the correct answer in the text in two popular Question Answering datasets used for passage re-ranking. The excessive favoring of earlier positions inside passages is an unwanted artefact. This leads to three common Transformer-based re-ranking models to ignore relevant parts in unseen passages. More concerningly, as the evaluation set is taken from the same biased distribution, the models overfitting to that bias overestimate their true effectiveness. In this work we analyze position bias on datasets, the contextualized representations, and their effect on retrieval results. We propose a debiasing method for retrieval datasets. Our results show that a model trained on a position-biased dataset exhibits a significant decrease in re-ranking effectiveness when evaluated on a debiased dataset. We demonstrate that by mitigating the position bias, Transformer-based re-ranking models are equally effective on a biased and debiased dataset, as well as more effective in a transfer-learning setting between two differently biased datasets.
| Type: | Proceedings paper |
|---|---|
| Title: | Mitigating the Position Bias of Transformer Models in Passage Re-Ranking |
| Event: | ECIR 2021 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-72113-8_16 |
| Publisher version: | https://doi.org/10.1007/978-3-030-72113-8_16 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10119400 |
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