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Anserini Regressions: TREC 2021 Deep Learning Track (Document)

Model: uniCOIL (without any expansions) zero-shot on segmented documents

This page describes experiments, integrated into Anserini's regression testing framework, on the TREC 2021 Deep Learning Track document ranking task using the MS MARCO V2 segmented document collection. Here, we cover experiments with the uniCOIL model trained on the MS MARCO V1 passage ranking test collection, applied in a zero-shot manner, without any expansions.

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.

Note that the NIST relevance judgments provide far more relevant documents per topic, unlike the "sparse" judgments provided by Microsoft (these are sometimes called "dense" judgments to emphasize this contrast). For additional instructions on working with MS MARCO V2 document collection, refer to this page.

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.

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 dl21-doc-segmented-unicoil-noexp-0shot

Corpus

Download, unpack, and prepare the corpus:

# Download
wget https://rgw.cs.uwaterloo.ca/JIMMYLIN-bucket0/data/msmarco_v2_doc_segmented_unicoil_noexp_0shot.tar -P collections/

# Unpack
tar -xvf collections/msmarco_v2_doc_segmented_unicoil_noexp_0shot.tar -C collections/

# Rename (indexer is expecting corpus under a slightly different name)
mv collections/msmarco_v2_doc_segmented_unicoil_noexp_0shot collections/msmarco-v2-doc-segmented-unicoil-noexp-0shot

To confirm, msmarco_v2_doc_segmented_unicoil_noexp_0shot.tar is 54 GB and has an MD5 checksum of 28261587d6afde56efd8df4f950e7fb4.

Indexing

Sample indexing command:

target/appassembler/bin/IndexCollection \
  -collection JsonVectorCollection \
  -input /path/to/msmarco-v2-doc-segmented-unicoil-noexp-0shot \
  -index indexes/lucene-index.msmarco-v2-doc-segmented-unicoil-noexp-0shot/ \
  -generator DefaultLuceneDocumentGenerator \
  -threads 18 -impact -pretokenized \
  >& logs/log.msmarco-v2-doc-segmented-unicoil-noexp-0shot &

The path /path/to/msmarco-v2-doc-segmented-unicoil-noexp-0shot/ 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 124,131,404 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 57 topics for which NIST has provided judgments as part of the TREC 2021 Deep Learning Track. The original data can be found here.

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

target/appassembler/bin/SearchCollection \
  -index indexes/lucene-index.msmarco-v2-doc-segmented-unicoil-noexp-0shot/ \
  -topics src/main/resources/topics-and-qrels/topics.dl21.unicoil-noexp.0shot.tsv.gz \
  -topicreader TsvInt \
  -output runs/run.msmarco-v2-doc-segmented-unicoil-noexp-0shot.unicoil-noexp-0shot.topics.dl21.unicoil-noexp.0shot.txt \
  -hits 10000 -selectMaxPassage -selectMaxPassage.delimiter "#" -selectMaxPassage.hits 1000 -impact -pretokenized &

Evaluation can be performed using trec_eval:

tools/eval/trec_eval.9.0.4/trec_eval -c -M 100 -m map src/main/resources/topics-and-qrels/qrels.dl21-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-noexp-0shot.unicoil-noexp-0shot.topics.dl21.unicoil-noexp.0shot.txt
tools/eval/trec_eval.9.0.4/trec_eval -c -m recall.100 src/main/resources/topics-and-qrels/qrels.dl21-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-noexp-0shot.unicoil-noexp-0shot.topics.dl21.unicoil-noexp.0shot.txt
tools/eval/trec_eval.9.0.4/trec_eval -c -m recall.1000 src/main/resources/topics-and-qrels/qrels.dl21-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-noexp-0shot.unicoil-noexp-0shot.topics.dl21.unicoil-noexp.0shot.txt
tools/eval/trec_eval.9.0.4/trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 src/main/resources/topics-and-qrels/qrels.dl21-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-noexp-0shot.unicoil-noexp-0shot.topics.dl21.unicoil-noexp.0shot.txt

Effectiveness

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

MAP@100 uniCOIL (noexp) zero-shot
DL21 (Doc) 0.2475
MRR@100 uniCOIL (noexp) zero-shot
DL21 (Doc) 0.9122
nDCG@10 uniCOIL (noexp) zero-shot
DL21 (Doc) 0.6282
R@100 uniCOIL (noexp) zero-shot
DL21 (Doc) 0.3497
R@1000 uniCOIL (noexp) zero-shot
DL21 (Doc) 0.6767

This run roughly corresponds to run p_unicoil0 submitted to the TREC 2021 Deep Learning Track under the "baseline" group. The difference is that here we are using pre-encoded queries, whereas the official submission performed query encoding on the fly.

Reproduction Log*

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