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Anserini Regressions: MS MARCO Document Ranking

Model: uniCOIL (with doc2query-T5 expansions) on segmented documents

This page describes regression experiments, integrated into Anserini's regression testing framework, using uniCOIL (with doc2query-T5 expansions) on the MS MARCO document ranking task. The uniCOIL model is described in the following paper:

Jimmy Lin and Xueguang Ma. A Few Brief Notes on DeepImpact, COIL, and a Conceptual Framework for Information Retrieval Techniques. arXiv:2106.14807.

The experiments on this page are not actually reported in the paper. However, the model is the same, applied to the MS MARCO segmented document corpus (with doc2query-T5 expansions). Retrieval uses MaxP technique, where we select the score of the highest-scoring passage from a document as the score for that document to produce a document ranking.

The exact configurations for these regressions are stored in this YAML file. Note that this page is automatically generated from this template as part of Anserini's regression pipeline, so do not modify this page directly; modify the template instead and then run bin/build.sh to rebuild the documentation.

From one of our Waterloo servers (e.g., orca), the following command will perform the complete regression, end to end:

python src/main/python/run_regression.py --index --verify --search --regression msmarco-doc-segmented-unicoil

Corpus

We make available a version of the MS MARCO passage corpus that has already been processed with uniCOIL, i.e., gone through document expansion and term reweighting. Thus, no neural inference is involved. For details on how to train uniCOIL and perform inference, please see this guide.

Download the corpus and unpack into collections/:

wget https://rgw.cs.uwaterloo.ca/JIMMYLIN-bucket0/data/msmarco-doc-segmented-unicoil.tar -P collections/

tar xvf collections/msmarco-doc-segmented-unicoil.tar -C collections/

To confirm, msmarco-doc-segmented-unicoil.tar is 18 GB and has MD5 checksum 6a00e2c0c375cb1e52c83ae5ac377ebb.

With the corpus downloaded, the following command will perform the complete regression, end to end, on any machine:

python src/main/python/run_regression.py --index --verify --search --regression msmarco-doc-segmented-unicoil \
  --corpus-path collections/msmarco-doc-segmented-unicoil

Alternatively, you can simply copy/paste from the commands below and obtain the same results.

Indexing

Sample indexing command:

target/appassembler/bin/IndexCollection \
  -collection JsonVectorCollection \
  -input /path/to/msmarco-doc-segmented-unicoil \
  -index indexes/lucene-index.msmarco-doc-segmented-unicoil/ \
  -generator DefaultLuceneDocumentGenerator \
  -threads 16 -impact -pretokenized \
  >& logs/log.msmarco-doc-segmented-unicoil &

The directory /path/to/msmarco-doc-segmented-unicoil/ should point to the corpus downloaded above.

The important indexing options to note here are -impact -pretokenized: the first tells Anserini not to encode BM25 doclengths into Lucene's norms (which is the default) and the second option says not to apply any additional tokenization on the uniCOIL tokens. Upon completion, we should have an index with 20,545,677 documents.

For additional details, see explanation of common indexing options.

Retrieval

Topics and qrels are stored in src/main/resources/topics-and-qrels/. The regression experiments here evaluate on the 6980 dev set questions; see this page for more details.

After indexing has completed, you should be able to perform retrieval as follows:

target/appassembler/bin/SearchCollection \
  -index indexes/lucene-index.msmarco-doc-segmented-unicoil/ \
  -topics src/main/resources/topics-and-qrels/topics.msmarco-doc.dev.unicoil.tsv.gz \
  -topicreader TsvInt \
  -output runs/run.msmarco-doc-segmented-unicoil.unicoil.topics.msmarco-doc.dev.unicoil.txt \
  -impact -pretokenized -hits 10000 -selectMaxPassage -selectMaxPassage.delimiter "#" -selectMaxPassage.hits 1000 &

Evaluation can be performed using trec_eval:

tools/eval/trec_eval.9.0.4/trec_eval -c -m map src/main/resources/topics-and-qrels/qrels.msmarco-doc.dev.txt runs/run.msmarco-doc-segmented-unicoil.unicoil.topics.msmarco-doc.dev.unicoil.txt
tools/eval/trec_eval.9.0.4/trec_eval -c -M 100 -m recip_rank src/main/resources/topics-and-qrels/qrels.msmarco-doc.dev.txt runs/run.msmarco-doc-segmented-unicoil.unicoil.topics.msmarco-doc.dev.unicoil.txt
tools/eval/trec_eval.9.0.4/trec_eval -c -m recall.100 src/main/resources/topics-and-qrels/qrels.msmarco-doc.dev.txt runs/run.msmarco-doc-segmented-unicoil.unicoil.topics.msmarco-doc.dev.unicoil.txt
tools/eval/trec_eval.9.0.4/trec_eval -c -m recall.1000 src/main/resources/topics-and-qrels/qrels.msmarco-doc.dev.txt runs/run.msmarco-doc-segmented-unicoil.unicoil.topics.msmarco-doc.dev.unicoil.txt

Effectiveness

With the above commands, you should be able to reproduce the following results:

AP@1000 uniCOIL (with doc2query-T5 expansions)
MS MARCO Doc: Dev 0.3535
RR@100 uniCOIL (with doc2query-T5 expansions)
MS MARCO Doc: Dev 0.3531
R@100 uniCOIL (with doc2query-T5 expansions)
MS MARCO Doc: Dev 0.8858
R@1000 uniCOIL (with doc2query-T5 expansions)
MS MARCO Doc: Dev 0.9546

This model corresponds to the run named "uniCOIL-d2q" on the official MS MARCO Document Ranking Leaderboard, submitted 2021/09/16. The following command generates a comparable run:

target/appassembler/bin/SearchCollection \
  -index indexes/lucene-index.msmarco-doc-segmented-unicoil/ \
  -topics src/main/resources/topics-and-qrels/topics.msmarco-doc.dev.unicoil.tsv.gz \
  -topicreader TsvInt \
  -output runs/run.msmarco-doc-segmented-unicoil.msmarco-doc.dev.txt \
  -format msmarco \
  -impact -pretokenized -hits 10000 -selectMaxPassage -selectMaxPassage.delimiter "#" -selectMaxPassage.hits 100

Note that the above command uses -format msmarco to directly generate a run in the MS MARCO output format. And to evaluate:

python tools/scripts/msmarco/msmarco_doc_eval.py \
  --judgments src/main/resources/topics-and-qrels/qrels.msmarco-doc.dev.txt \
  --run runs/run.msmarco-doc-segmented-unicoil.msmarco-doc.dev.txt

The results should be as follows:

#####################
MRR @100: 0.352997702662614
QueriesRanked: 5193
#####################

Reproduction Log*

To add to this reproduction log, modify this template and run bin/build.sh to rebuild the documentation.