From 43ad899337ac5e3b219d899bb218c4bcae18b1e6 Mon Sep 17 00:00:00 2001 From: Jimmy Lin Date: Wed, 14 Jul 2021 16:37:26 -0400 Subject: [PATCH] Minor tweaks to DeepImpact and uniCOIL docs: added links to Pyserini (#1597) --- docs/experiments-msmarco-passage-deepimpact.md | 3 ++- docs/experiments-msmarco-passage-unicoil.md | 4 +++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/docs/experiments-msmarco-passage-deepimpact.md b/docs/experiments-msmarco-passage-deepimpact.md index 4e2f7468b2..d431c4d2da 100644 --- 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 index 843d8e97c8..cef2bdf931 100644 --- 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