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regressions-car17v2.0.md

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Anserini Regressions: CAR17 (v2.0)

Models: various bag-of-words approaches

This page documents regression experiments for the TREC 2017 Complex Answer Retrieval (CAR) section-level passage retrieval task (v2.0). 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 car17v2.0

Indexing

Typical indexing command:

bin/run.sh io.anserini.index.IndexCollection \
  -threads 1 \
  -collection CarCollection \
  -input /path/to/car-paragraphCorpus.v2.0 \
  -generator DefaultLuceneDocumentGenerator \
  -index indexes/lucene-index.car-paragraphCorpus.v2.0/ \
  -storePositions -storeDocvectors -storeRaw \
  >& logs/log.car-paragraphCorpus.v2.0 &

The directory /path/to/car17v2.0 should be the root directory of Complex Answer Retrieval (CAR) paragraph corpus (v2.0), which can be downloaded here.

For additional details, see explanation of common indexing options.

Retrieval

The "benchmarkY1-test" topics and qrels (v2.0) are stored here, which is linked to the Anserini repo as a submodule. They are downloaded from the CAR website:

Specifically, this is the section-level passage retrieval task with automatic ground truth.

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

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-index.car-paragraphCorpus.v2.0/ \
  -topics tools/topics-and-qrels/topics.car17v2.0.benchmarkY1test.txt \
  -topicReader Car \
  -output runs/run.car-paragraphCorpus.v2.0.bm25.topics.car17v2.0.benchmarkY1test.txt \
  -bm25 &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-index.car-paragraphCorpus.v2.0/ \
  -topics tools/topics-and-qrels/topics.car17v2.0.benchmarkY1test.txt \
  -topicReader Car \
  -output runs/run.car-paragraphCorpus.v2.0.bm25+rm3.topics.car17v2.0.benchmarkY1test.txt \
  -bm25 -rm3 &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-index.car-paragraphCorpus.v2.0/ \
  -topics tools/topics-and-qrels/topics.car17v2.0.benchmarkY1test.txt \
  -topicReader Car \
  -output runs/run.car-paragraphCorpus.v2.0.bm25+ax.topics.car17v2.0.benchmarkY1test.txt \
  -bm25 -axiom -axiom.deterministic -rerankCutoff 20 &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-index.car-paragraphCorpus.v2.0/ \
  -topics tools/topics-and-qrels/topics.car17v2.0.benchmarkY1test.txt \
  -topicReader Car \
  -output runs/run.car-paragraphCorpus.v2.0.ql.topics.car17v2.0.benchmarkY1test.txt \
  -qld &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-index.car-paragraphCorpus.v2.0/ \
  -topics tools/topics-and-qrels/topics.car17v2.0.benchmarkY1test.txt \
  -topicReader Car \
  -output runs/run.car-paragraphCorpus.v2.0.ql+rm3.topics.car17v2.0.benchmarkY1test.txt \
  -qld -rm3 &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-index.car-paragraphCorpus.v2.0/ \
  -topics tools/topics-and-qrels/topics.car17v2.0.benchmarkY1test.txt \
  -topicReader Car \
  -output runs/run.car-paragraphCorpus.v2.0.ql+ax.topics.car17v2.0.benchmarkY1test.txt \
  -qld -axiom -axiom.deterministic -rerankCutoff 20 &

Evaluation can be performed using trec_eval:

bin/trec_eval -c -m map -c -m recip_rank tools/topics-and-qrels/qrels.car17v2.0.benchmarkY1test.txt runs/run.car-paragraphCorpus.v2.0.bm25.topics.car17v2.0.benchmarkY1test.txt

bin/trec_eval -c -m map -c -m recip_rank tools/topics-and-qrels/qrels.car17v2.0.benchmarkY1test.txt runs/run.car-paragraphCorpus.v2.0.bm25+rm3.topics.car17v2.0.benchmarkY1test.txt

bin/trec_eval -c -m map -c -m recip_rank tools/topics-and-qrels/qrels.car17v2.0.benchmarkY1test.txt runs/run.car-paragraphCorpus.v2.0.bm25+ax.topics.car17v2.0.benchmarkY1test.txt

bin/trec_eval -c -m map -c -m recip_rank tools/topics-and-qrels/qrels.car17v2.0.benchmarkY1test.txt runs/run.car-paragraphCorpus.v2.0.ql.topics.car17v2.0.benchmarkY1test.txt

bin/trec_eval -c -m map -c -m recip_rank tools/topics-and-qrels/qrels.car17v2.0.benchmarkY1test.txt runs/run.car-paragraphCorpus.v2.0.ql+rm3.topics.car17v2.0.benchmarkY1test.txt

bin/trec_eval -c -m map -c -m recip_rank tools/topics-and-qrels/qrels.car17v2.0.benchmarkY1test.txt runs/run.car-paragraphCorpus.v2.0.ql+ax.topics.car17v2.0.benchmarkY1test.txt

Effectiveness

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

MAP BM25 +RM3 +Ax QL +RM3 +Ax
TREC 2017 CAR: benchmarkY1test (v2.0) 0.1545 0.1308 0.1364 0.1371 0.1083 0.1077
MRR BM25 +RM3 +Ax QL +RM3 +Ax
TREC 2017 CAR: benchmarkY1test (v2.0) 0.2321 0.1940 0.1978 0.2013 0.1608 0.1588