NCLPRF

Neural PRF based re-ranking on CLIR

You can find our NCLPRF paper here

Data Preparation

Training Data

We used CLIRMatrix to train the bilingual XLM-R model. For the corresponding Persian(fa), Russian(ru), and Chinese(zh), we downloaded the bilingual pairs of query and passages to train the model. Use the script train_data_download.py to download the bilingual pairs with relevance labels of 6, 5, and 4. Then we used pairs of relevance 6 for positive pairs and remaning pairs of relevances of 5 and 4 for pseudo-relevance feedback (PRF) signals.

$ python3 train_data_download.py --outDir ./irds_out/ --dataDir ./ --clir_matrix_fname irds_list.txt --relevance_score 6

$ python github_preparate_data.py --write_data_dir=ProcessedData --data_root=./irds_out --lang=ru

Testing Data

We used following test collections for those corresponding langauges.

1. CLEF Persian
2. CLEF Russian
3. NTCIR Chinese

Baseline

The baseline is a casted monolingual BM25 and RM3 search. We used Patapsco tool to get the baseline scores. To get the machine translated queries from English to corresponding languages, we used COE's internal NMT system.

After installing Patapsco and changing the flags in the config.yml file to run baseline search, run the following command. To run RM3, switch the retrieve/rm3 flag to True. $ patapsco config.yml

Training

We used train/train.py script to train NCLPRF model with 1 or 2 PRF documents using different vector aggregation weightings.

Testing

We used test/test.py script to do the inference and evaluate the validation and test collections over the loop of checkpoint files saved during the training iterations. ir-measures library is used to evaluate the model performance.

author = {Chandradevan, Ramraj and Yang, Eugene and Yarmohammadi, Mahsa and Agichtein, Eugene},
title = {Learning to Enrich Query Representation with Pseudo-Relevance Feedback for Cross-Lingual Retrieval},
year = {2022},
doi = {10.1145/3477495.3532013},
booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {1790–1795},
numpages = {6},
location = {Madrid, Spain},
series = {SIGIR '22}
}```