| # Scores of generated queries | |
| This repo contains the scores files pertaining to [this study](https://github.com/Watheq9/d2qminus-repro). In particular, we scored the expansion queries generated by [T5-based Doc2Query model](https://huggingface.co/castorini/monot5-base-msmarco) for MSMARCO-v1 passage dataset and a subset of BEIR benchemark. | |
| We used [ELECTRA](https://huggingface.co/crystina-z/monoELECTRA_LCE_nneg31) cross-encoder to get the relevance scores between the document text and its expansion queries. More details in the study repo [here](https://github.com/Watheq9/d2qminus-repro). | |
| ## Structure | |
| All files are .jsonl files with the following three columns per line: ["id", "predicted_queries","querygen_score"]. | |
| So, each file contains the document id, the expansion queries and their corresponding ELECTRA relevance scores. | |
| Here are the matching of each dataset: | |
| `msmarco-v1-80-scored-queries.jsonl` is for MSMarco-v1 dataset. | |
| `dbpedia-20-scored-queries.jsonl` is for DBPedia dataset. | |
| `quora-20-scored-queries.jsonl` is for Quora dataset. | |
| `robust04-20-scored-queries.jsonl` is for Robust04 dataset. | |
| `trec-covid-20-scored-queries.jsonl` is for TREC-COVID dataset. | |
| `webis-touche2020-20-scored-queries.jsonl` is for Touché-2020 dataset. | |
| ## Credit | |
| The N=80 expansion queries of MSMARCO-v1 were copied from this [repository](https://github.com/castorini/docTTTTTquery). Please cite their work. | |
| The N=20 expansion queries of BEIR benchemark were copied from this [repository](https://huggingface.co/income). Please cite their work. | |
| ## Citation | |
| If you used any piece of this repository, please consider citing our work: | |
| ```plaintext | |
| @inproceedings{mansour2024revisit, | |
| title={Revisiting Document Expansion and Filtering for Effective First-Stage Retrieval}, | |
| author={Mansour, Watheq and Zhuang, Shengyao and Zhuang, Guido and Mackenzie, Joel}, | |
| booktitle = {Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval}, | |
| year={2024}, | |
| publisher = {Association for Computing Machinery}, | |
| series = {SIGIR '24} | |
| } | |
| ``` | |
| --- | |
| license: cc-by-4.0 | |
| --- | |