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Anserini Regressions: TREC 2022 Deep Learning Track (Document)

Model: uniCOIL (with doc2query-T5 expansions) zero-shot on segmented documents (title/segment encoding)

This page describes experiments, integrated into Anserini's regression testing framework, on the TREC 2022 Deep Learning Track document ranking task using the MS MARCO V2 segmented document corpus. Here, we cover experiments with the uniCOIL model trained on the MS MARCO V1 passage ranking test collection, applied in a zero-shot manner, with doc2query-T5 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: As an important detail, there is the question of what text we feed into the encoder to generate document representations. Initially, we fed only the segment text, but later we realized that prepending the title of the document improves effectiveness. This regression captures the latter title/segment encoding, which for clarity we call v2, distinguished from segment-only encoding, which does not have a corresponding regression.

For additional instructions on working with the MS MARCO V2 document corpus, refer to this page.

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). An important caveat is that these document judgments were inferred from the passages. That is, if a passage is relevant, the document containing it is considered relevant.

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 dl22-doc-segmented.unicoil-0shot-v2.cached

We make available a version of the MS MARCO document corpus that has already been processed with uniCOIL (per above), i.e., we have applied doc2query-T5 expansions, performed model inference on every document, and stored the output sparse vectors. Thus, no neural inference is involved.

From any machine, the following command will download the corpus and perform the complete regression, end to end:

python src/main/python/run_regression.py --download --index --verify --search --regression dl22-doc-segmented.unicoil-0shot-v2.cached

The run_regression.py script automates the following steps, but if you want to perform each step manually, simply copy/paste from the commands below and you'll obtain the same regression results.

Corpus Download

Download, unpack, and prepare the corpus:

# Download
wget https://rgw.cs.uwaterloo.ca/JIMMYLIN-bucket0/data/msmarco_v2_doc_segmented_unicoil_0shot_v2.tar -P collections/

# Unpack
tar -xvf collections/msmarco_v2_doc_segmented_unicoil_0shot_v2.tar -C collections/

# Rename (indexer is expecting corpus under a slightly different name)
mv collections/msmarco_v2_doc_segmented_unicoil_0shot_v2 collections/msmarco-v2-doc-segmented-unicoil-0shot-v2

To confirm, msmarco_v2_doc_segmented_unicoil_0shot_v2.tar is 72 GB and has an MD5 checksum of c5639748c2cbad0152e10b0ebde3b804. With the corpus downloaded, the following command will perform the remaining steps below:

python src/main/python/run_regression.py --index --verify --search --regression dl22-doc-segmented.unicoil-0shot-v2.cached \
  --corpus-path collections/msmarco-v2-doc-segmented-unicoil-0shot-v2

Indexing

Sample indexing command:

bin/run.sh io.anserini.index.IndexCollection \
  -threads 24 \
  -collection JsonVectorCollection \
  -input /path/to/msmarco-v2-doc-segmented-unicoil-0shot-v2 \
  -generator DefaultLuceneDocumentGenerator \
  -index indexes/lucene-inverted.msmarco-v2-doc-segmented.unicoil-0shot-v2/ \
  -impact -pretokenized -storeRaw \
  >& logs/log.msmarco-v2-doc-segmented-unicoil-0shot-v2 &

The path /path/to/msmarco-v2-doc-segmented-unicoil-0shot-v2/ 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 124,131,414 documents.

For additional details, see explanation of common indexing options.

Retrieval

Topics and qrels are stored here, which is linked to the Anserini repo as a submodule. The regression experiments here evaluate on the 76 topics for which NIST has provided inferred judgments as part of the TREC 2022 Deep Learning Track.

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

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-inverted.msmarco-v2-doc-segmented.unicoil-0shot-v2/ \
  -topics tools/topics-and-qrels/topics.dl22.unicoil.0shot.tsv.gz \
  -topicReader TsvInt \
  -output runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached.topics.dl22.unicoil.0shot.txt \
  -hits 10000 -selectMaxPassage -selectMaxPassage.delimiter "#" -selectMaxPassage.hits 1000 -impact -pretokenized &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-inverted.msmarco-v2-doc-segmented.unicoil-0shot-v2/ \
  -topics tools/topics-and-qrels/topics.dl22.unicoil.0shot.tsv.gz \
  -topicReader TsvInt \
  -output runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached+rm3.topics.dl22.unicoil.0shot.txt \
  -hits 10000 -selectMaxPassage -selectMaxPassage.delimiter "#" -selectMaxPassage.hits 1000 -impact -pretokenized -rm3 -collection JsonVectorCollection &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-inverted.msmarco-v2-doc-segmented.unicoil-0shot-v2/ \
  -topics tools/topics-and-qrels/topics.dl22.unicoil.0shot.tsv.gz \
  -topicReader TsvInt \
  -output runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached+rocchio.topics.dl22.unicoil.0shot.txt \
  -hits 10000 -selectMaxPassage -selectMaxPassage.delimiter "#" -selectMaxPassage.hits 1000 -impact -pretokenized -rocchio -collection JsonVectorCollection &

Evaluation can be performed using trec_eval:

bin/trec_eval -c -M 100 -m map tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached.topics.dl22.unicoil.0shot.txt
bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached.topics.dl22.unicoil.0shot.txt
bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached.topics.dl22.unicoil.0shot.txt
bin/trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached.topics.dl22.unicoil.0shot.txt

bin/trec_eval -c -M 100 -m map tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached+rm3.topics.dl22.unicoil.0shot.txt
bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached+rm3.topics.dl22.unicoil.0shot.txt
bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached+rm3.topics.dl22.unicoil.0shot.txt
bin/trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached+rm3.topics.dl22.unicoil.0shot.txt

bin/trec_eval -c -M 100 -m map tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached+rocchio.topics.dl22.unicoil.0shot.txt
bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached+rocchio.topics.dl22.unicoil.0shot.txt
bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached+rocchio.topics.dl22.unicoil.0shot.txt
bin/trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-unicoil-0shot-v2.unicoil-0shot-cached+rocchio.topics.dl22.unicoil.0shot.txt

Effectiveness

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

MAP@100 uniCOIL (with doc2query-T5) zero-shot +RM3 +Rocchio
DL22 (Doc) 0.1400 0.1598 0.1701
MRR@100 uniCOIL (with doc2query-T5) zero-shot +RM3 +Rocchio
DL22 (Doc) 0.8170 0.8293 0.8315
nDCG@10 uniCOIL (with doc2query-T5) zero-shot +RM3 +Rocchio
DL22 (Doc) 0.4451 0.4570 0.4767
R@100 uniCOIL (with doc2query-T5) zero-shot +RM3 +Rocchio
DL22 (Doc) 0.2656 0.2810 0.2991
R@1000 uniCOIL (with doc2query-T5) zero-shot +RM3 +Rocchio
DL22 (Doc) 0.5235 0.5586 0.5783