updates.

castorini · lintool · Jun 14, 2021 · Jun 2, 2021 · Jun 4, 2021 · Jun 4, 2021
commit 86eab751a0fe649903cd9850ede2bf0a98d7bb2f
diff --git a/docs/regressions-dl19-doc.md b/docs/regressions-dl19-doc.md
@@ -126,7 +126,7 @@ Note that retrieval metrics are computed to depth 100 hits per query (as opposed
 Also, remember that we keep qrels of _all_ relevance grades, unlike the case for DL19 passage ranking, where relevance grade 1 needs to be discarded when computing certain metrics.
 These results correspond to the Anserini baselines reported in the [track overview paper](https://arxiv.org/abs/2003.07820).
 
-Some of these regressions correspond to official TREC 2020 Deep Learning Track submissions by `BASELINE` group:
+These regressions correspond to official TREC 2019 Deep Learning Track submissions by `BASELINE` group:
 
 + `bm25base` = BM25 (Default), `k1=0.9`, `b=0.4`
 + `bm25base_rm3` = BM25 (Default) + RM3, `k1=0.9`, `b=0.4`

diff --git a/docs/regressions-dl19-passage.md b/docs/regressions-dl19-passage.md
@@ -136,7 +136,7 @@ Note that retrieval metrics are computed to depth 1000 hits per query (as oppose
 Also, for computing nDCG, remember that we keep qrels of _all_ relevance grades, whereas for other metrics (e.g., MAP), relevance grade 1 is considered not relevant (i.e., use the `-l 2` option in `trec_eval`).
 These results correspond to the Anserini baselines reported in the [track overview paper](https://arxiv.org/abs/2003.07820).
 
-Some of these regressions correspond to official TREC 2020 Deep Learning Track submissions by `BASELINE` group:
+These regressions correspond to official TREC 2019 Deep Learning Track submissions by `BASELINE` group:
 
 + `bm25base_p` = BM25 (Default), `k1=0.9`, `b=0.4`
 + `bm25base_rm3_p` = BM25 (Default) + RM3, `k1=0.9`, `b=0.4`

diff --git a/src/main/resources/docgen/templates/dl19-doc.template b/src/main/resources/docgen/templates/dl19-doc.template
@@ -57,7 +57,7 @@ Note that retrieval metrics are computed to depth 100 hits per query (as opposed
 Also, remember that we keep qrels of _all_ relevance grades, unlike the case for DL19 passage ranking, where relevance grade 1 needs to be discarded when computing certain metrics.
 These results correspond to the Anserini baselines reported in the [track overview paper](https://arxiv.org/abs/2003.07820).
 
-Some of these regressions correspond to official TREC 2020 Deep Learning Track submissions by `BASELINE` group:
+These regressions correspond to official TREC 2019 Deep Learning Track submissions by `BASELINE` group:
 
 + `bm25base` = BM25 (Default), `k1=0.9`, `b=0.4`
 + `bm25base_rm3` = BM25 (Default) + RM3, `k1=0.9`, `b=0.4`

diff --git a/src/main/resources/docgen/templates/dl19-passage.template b/src/main/resources/docgen/templates/dl19-passage.template
@@ -51,7 +51,7 @@ Note that retrieval metrics are computed to depth 1000 hits per query (as oppose
 Also, for computing nDCG, remember that we keep qrels of _all_ relevance grades, whereas for other metrics (e.g., MAP), relevance grade 1 is considered not relevant (i.e., use the `-l 2` option in `trec_eval`).
 These results correspond to the Anserini baselines reported in the [track overview paper](https://arxiv.org/abs/2003.07820).
 
-Some of these regressions correspond to official TREC 2020 Deep Learning Track submissions by `BASELINE` group:
+These regressions correspond to official TREC 2019 Deep Learning Track submissions by `BASELINE` group:
 
 + `bm25base_p` = BM25 (Default), `k1=0.9`, `b=0.4`
 + `bm25base_rm3_p` = BM25 (Default) + RM3, `k1=0.9`, `b=0.4`