ReInfoSelect

Codes and datasets of WWW2020 Paper Selective Weak Supervision for Neural Information Retrieval. Paper

Framework

Results

Model	Method	ClueWeb09 NDCG@20	ClueWeb09 ERR@20	Robust04 NDCG@20	Robust04 ERR@20	ClueWeb12 NDCG@20	ClueWeb12 ERR@20
Conv-KNRM	No Weak Supervision	0.2873	0.1597	0.4267	0.1168	0.1123	0.0915
	ReInfoSelect	0.3096	0.1611	0.4423	0.1202	0.1225	0.1044
TK	No Weak Supervision	0.3003	0.1577	0.4273	0.1163	0.1192	0.0991
	ReInfoSelect	0.3103	0.1626	0.4320	0.1183	0.1297	0.1043
EDRM	No Weak Supervision	0.2922	0.1642	0.4263	0.1158	0.1119	0.0910
	ReInfoSelect	0.3163	0.1794	0.4396	0.1208	0.1215	0.0980
BERT	No Weak Supervision	0.2999	0.1631	0.4258	0.1163	0.1190	0.0963
	ReInfoSelect	0.3261	0.1669	0.4500	0.1220	0.1276	0.0997

More results are available in results.

Datasets

Data can be downloaded from Datasets.

Datasets	Queries/Anchors	Query/Anchor-Doc Pairs	Released Files
Weak Supervision	100K	6.75M	Anchors, A-D Relations
ClueWeb09-B	200	47.1K	Queries, Q-D Relations, SDM scores
Robust04	249	311K	Queries, Q-D Relations, SDM scores
ClueWeb12-B13	100	28.9K	Queries, Q-D Relations, SDM scores

As we cannot release the document contents, the document IDs are used instead.

Requirements

Setup requirements directly

• python == 3.7
• torch >= 1.0.0

To run ReInfoSelect, please install all requirements.

pip install -r requirements.txt

Use docker

cd docker
docker build -t reinfoselect_official:v0.1 .

data

wget http://nlp.stanford.edu/data/glove.6B.zip -P ./data
unzip ./data/glove.6B.zip -d ./data
wget https://msmarco.blob.core.windows.net/msmarcoranking/triples.train.small.tar.gz -P ./data
tar -zxvf triples.train.small.tar.gz -C ./data

Run

First, please prepare your data in recommended format. The MS MARCO or ClueWeb09 corpus may be too large for your machine, you can change -max_input to set max input numbers of training instances.

For cknrm training, set -model to cknrm, for bert training, set to bert, the batch_size for bert may need to set smaller.

sh train.sh

Then, concatenate the neural features with retrieval (SDM or BM25) score and run Coor-Ascent using RankLib.

sh coor_ascent.sh

For inference:

sh inference.sh

For ensemble:

sh ensemble.sh

Citation

Please cite our paper if you find it helpful.

@inproceedings{Zhang2020SelectiveWS,
    title = {Selective Weak Supervision for Neural Information Retrieval},
    author = {Kaitao Zhang and Chenyan Xiong and Zhenghao Liu and Zhiyuan Liu},
    booktitle = {{WWW} '20: The Web Conference 2020},
    year = {2020}
}

Contact

If you have any questions, suggestions and bug reports, please email [email protected].