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Anserini Regressions: MS MARCO Passage Ranking

Models: BM25 with (vanilla) doc2query expansions

This page documents regression experiments on the MS MARCO passage ranking task with BM25 on (vanilla) doc2query (also called doc2query-base) expansions, as proposed in the following paper:

Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. Document Expansion by Query Prediction. arXiv:1904.08375, 2019.

These experiments are integrated into Anserini's regression testing framework. For more complete instructions on how to run end-to-end experiments, 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 msmarco-v1-passage.doc2query

Indexing

Typical indexing command:

bin/run.sh io.anserini.index.IndexCollection \
  -threads 9 \
  -collection JsonCollection \
  -input /path/to/msmarco-passage-doc2query \
  -generator DefaultLuceneDocumentGenerator \
  -index indexes/lucene-inverted.msmarco-v1-passage.doc2query/ \
  -storePositions -storeDocvectors -storeRaw \
  >& logs/log.msmarco-passage-doc2query &

The directory /path/to/msmarco-passage-doc2query should be a directory containing jsonl files containing the expanded passage collection. This page explains how to perform this data preparation.

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 6980 dev set questions; see this page for more details.

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-v1-passage.doc2query/ \
  -topics tools/topics-and-qrels/topics.msmarco-passage.dev-subset.txt \
  -topicReader TsvInt \
  -output runs/run.msmarco-passage-doc2query.bm25-default.topics.msmarco-passage.dev-subset.txt \
  -bm25 &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-inverted.msmarco-v1-passage.doc2query/ \
  -topics tools/topics-and-qrels/topics.msmarco-passage.dev-subset.txt \
  -topicReader TsvInt \
  -output runs/run.msmarco-passage-doc2query.bm25-tuned.topics.msmarco-passage.dev-subset.txt \
  -bm25 -bm25.k1 0.82 -bm25.b 0.68 &

Evaluation can be performed using trec_eval:

bin/trec_eval -c -m map tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage-doc2query.bm25-default.topics.msmarco-passage.dev-subset.txt
bin/trec_eval -c -M 10 -m recip_rank tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage-doc2query.bm25-default.topics.msmarco-passage.dev-subset.txt
bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage-doc2query.bm25-default.topics.msmarco-passage.dev-subset.txt
bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage-doc2query.bm25-default.topics.msmarco-passage.dev-subset.txt

bin/trec_eval -c -m map tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage-doc2query.bm25-tuned.topics.msmarco-passage.dev-subset.txt
bin/trec_eval -c -M 10 -m recip_rank tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage-doc2query.bm25-tuned.topics.msmarco-passage.dev-subset.txt
bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage-doc2query.bm25-tuned.topics.msmarco-passage.dev-subset.txt
bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage-doc2query.bm25-tuned.topics.msmarco-passage.dev-subset.txt

Effectiveness

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

AP@1000 BM25 (default) BM25 (tuned)
MS MARCO Passage: Dev 0.2270 0.2293
RR@10 BM25 (default) BM25 (tuned)
MS MARCO Passage: Dev 0.2189 0.2213
R@100 BM25 (default) BM25 (tuned)
MS MARCO Passage: Dev 0.7133 0.7171
R@1000 BM25 (default) BM25 (tuned)
MS MARCO Passage: Dev 0.8900 0.8911

Explanation of settings:

  • The setting "default" refers the default BM25 settings of k1=0.9, b=0.4.
  • The setting "tuned" refers to k1=0.82, b=0.68, tuned on on the original passages, as described in this page.

Additional Implementation Details

Note that prior to December 2021, runs generated with SearchCollection in the TREC format and then converted into the MS MARCO format give slightly different results from runs generated by SearchMsmarco directly in the MS MARCO format, due to tie-breaking effects. This was fixed with #1458, which also introduced (intra-configuration) multi-threading. As a result, SearchMsmarco has been deprecated and replaced by SearchCollection; both have been verified to generate identical output.

The commands below have been retained for historical reasons only.

The following command generates with SearchMsmarco the run denoted "BM25 (tuned)" above (k1=0.82, b=0.68):

$ sh target/appassembler/bin/SearchMsmarco -hits 1000 -threads 8 \
    -index indexes/lucene-index.msmarco-passage-doc2query.pos+docvectors+raw \
    -queries collections/msmarco-passage/queries.dev.small.tsv \
    -k1 0.82 -b 0.68 \
    -output runs/run.msmarco-passage-doc2query

$ python tools/scripts/msmarco/msmarco_passage_eval.py \
   collections/msmarco-passage/qrels.dev.small.tsv runs/run.msmarco-passage-doc2query

#####################
MRR @10: 0.2213412471005586
QueriesRanked: 6980
#####################

Note that this run does not correspond to the scores reported in the paper that introduced doc2query:

Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. Document Expansion by Query Prediction. arXiv:1904.08375, 2019.

The scores reported in the above paper refer to entry "BM25 (Anserini) + doc2query" dated 2019/04/10 on the MS MARCO Passage Ranking Leaderboard. The paper/leaderboard run reports 0.215 MRR@10, which is slightly lower than the "BM25 (Tuned)" regression run above, due to an earlier version of Lucene (7.6) and use of default BM25 parameters.