Minor tweaks to DeepImpact and uniCOIL docs: added links to Pyserini (#…

…1597)
castorini · Jul 14, 2021 · 43ad899 · 43ad899
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diff --git a/docs/experiments-msmarco-passage-deepimpact.md b/docs/experiments-msmarco-passage-deepimpact.md
@@ -2,11 +2,12 @@
 
 This page describes how to reproduce the DeepImpact experiments in the following paper:
 
-> Antonio Mallia, Omar Khattab, Nicola Tonellotto, and Torsten Suel. [Learning Passage Impacts for Inverted Indexes.](https://arxiv.org/abs/2104.12016) _arXiv:2104.12016_.
+> Antonio Mallia, Omar Khattab, Nicola Tonellotto, and Torsten Suel. [Learning Passage Impacts for Inverted Indexes.](https://dl.acm.org/doi/10.1145/3404835.3463030) _SIGIR 2021_.
 
 Here, we start with a version of the MS MARCO passage corpus that has already been processed with DeepImpact, i.e., gone through document expansion and term reweighting.
 Thus, no neural inference is involved.
 
+Note that Pyserini provides [a comparable reproduction guide](https://github.com/castorini/pyserini/blob/master/docs/experiments-deepimpact.md), so if you don't like Java, you can get _exactly_ the same results from Python.
 
 ## Data Prep
 

diff --git a/docs/experiments-msmarco-passage-unicoil.md b/docs/experiments-msmarco-passage-unicoil.md
@@ -4,9 +4,11 @@ This page describes how to reproduce the uniCOIL experiments in the following pa
 
 > Jimmy Lin and Xueguang Ma. [A Few Brief Notes on DeepImpact, COIL, and a Conceptual Framework for Information Retrieval Techniques.](https://arxiv.org/abs/2106.14807) _arXiv:2106.14807_.
 
-Here, we start with a version of the MS MARCO passage corpus that has already been processed with uniCOIL, i.e., gone through document expansion and term reweighting.
+In this guide, we start with a version of the MS MARCO passage corpus that has already been processed with uniCOIL, i.e., gone through document expansion and term reweighting.
 Thus, no neural inference is involved.
+For details on how to train uniCOIL and perform inference, please see [this guide](https://github.com/luyug/COIL/tree/main/uniCOIL).
 
+Note that Pyserini provides [a comparable reproduction guide](https://github.com/castorini/pyserini/blob/master/docs/experiments-unicoil.md), so if you don't like Java, you can get _exactly_ the same results from Python.
 
 ## Data Prep