Overview paper: https://arxiv.org/pdf/2003.07820.pdf
Note: You are viewing the guidelines for the 2019 edition of the TREC Deep Learning track. Please visit https://microsoft.github.io/msmarco/TREC-Deep-Learning for the upcoming / latest edition of the track.
The Deep Learning Track studies information retrieval in a large training data regime. This is the case where the number of training queries with at least one positive label is at least in the tens of thousands, if not hundreds of thousands or more. This corresponds to real-world scenarios such as training based on click logs and training based on labels from shallow pools (such as the pooling in the TREC Million Query Track or the evaluation of search engines based on early precision).
Our main goal is to study what methods work best in this regime. For example, do the same methods that work on small data also work on large data? How much do methods improve when given more training data? What external data and weak supervision can be brought in to bear in this scenario, and how useful is it to combine full supervision with other forms of supervision including transfer learning?
Certain machine learning based methods, such as methods based on deep learning are known to require very large datasets for training. Lack of such large scale datasets has been a limitation for developing such methods for common information retrieval tasks, such as document ranking. One of the goals of the track is to make such large-scale datasets publicly available, which could enable the development of different machine learning architectures without being constrained by the amount of training data. Through the evaluation methodologies we release as part of the track, we also enable participants to compare the performance of their methods with other state of the art methods.
Another novel aspect of this track is its involvement in the Replicable Runs Initiative (RRI), an exercise to make submissions replicable so that other teams can produce the same output. This exercise is completely optional, but interested participants can (in the online submission form) designate any run to be an “RRI run”. The Docker images for replicating those runs will be due August 21 (two weeks after the run submission deadline). See a separate page dedicated to the Replicable Run Initiative (RRI) for additional details, including the specification of the Docker images.
The deep learning track has two tasks: Passage ranking and document ranking. You can submit up to three runs for each of these tasks.
Both tasks use a large human-generated set of training labels, from the MS-MARCO dataset. The two tasks use the same test queries. They also use the same form of training data with usually one positive training document/passage per training query. In the case of passage ranking, there is a direct human label that says the passage can be used to answer the query, whereas for training the document ranking task we transfer the same passage-level labels to document-level labels.
Below the two tasks are described in more detail.
The first task focuses on document ranking. We have two subtasks related to this: Full ranking and top-100 re-ranking.
In the full ranking (retrieval) subtask, you are expected to rank documents based on their relevance to the question, where documents can be retrieved from the full document collection provided. You can submit up to 1000 documents for this task. It models a scenario where you are building an end-to-end retrieval system.
In the re-ranking subtask, we provide you with an initial ranking of 100 documents from a simple IR system, and you are expected to re-rank the documents in terms of their relevance to the question. This is a very common real-world scenario, since many end-to-end systems are implemented as retrieval followed by top-k re-ranking. The re-ranking subtask allows participants to focus on re-ranking only, without needing to implement an end-to-end system. It also makes those re-ranking runs more comparable, because they all start from the same set of 100 candidates.
Similar to the document ranking task, the passage ranking task also has a full ranking and re-ranking subtasks.
In context of full ranking (retrieval) subtask, given a question, you are expected to rank passages from the full collection in terms of their likelihood of containing an answer to the question. You can submit up to 1000 passages for this end-to-end retrieval task.
In context of top-1000 re-ranking subtask, we provide you with an initial ranking of 1000 passages and you are expected to re-rank these passages based on their likelihood of containing an answer to the question. In this subtask, we can compare different re-ranking methods based on the same initial set of 1000 candidates, with the same rationale as described for the document re-ranking subtask.
You are allowed to use external information while developing your runs. When you submit your runs, please fill in a form listing what evidence you used, for example an external corpus such as Wikipedia or a pre-trained model (e.g. word embeddings).
When submitting runs, participants will be able to indicate what resources they used. This could include the provided set of document ranking training data, but also optionally other data such as the passage ranking task labels or external labels or pretrained models. This will allow us to analyze the runs and break they down into types.
IMPORTANT NOTE: It is prohibited to use evidence from the MS-MARCO Question Answering task in your submission. That dataset reveals some minor details of how the MS MARCO dataset was constructed that would not be available in a real-world search engine; hence, should be avoided.
This year we have a document ranking dataset and a passage ranking dataset. The two datasets will share the same set of test queries, which will be released later.
The document ranking dataset is based on source documents, which contained passages in the passage task. Although we have an incomplete set of documents that was gathered some time later than the passage data, the corpus is 3.2 million documents and our training set has 367,013 queries. For each training query, we map from a positive passage ID to the corresponding document ID in our 3.2 million. We do so on the assumption that a document that produced a relevant passage is usually a relevant document.
|Type||Filename||File size||Num Records||Format|
|Corpus||msmarco-docs.tsv||22 GB||3,213,835||tsv: docid, url, title, body|
|Corpus||msmarco-docs.trec||22 GB||3,213,835||TREC DOC format (same content as msmarco-docs.tsv)|
|Corpus||msmarco-docs-lookup.tsv||101 MB||3,213,835||tsv: docid, offset_trec, offset_tsv|
|Train||msmarco-doctrain-queries.tsv||15 MB||367,013||tsv: qid, query|
|Train||msmarco-doctrain-top100||1.8 GB||36,701,116||TREC submission: qid, “Q0”, docid, rank, score, runstring|
|Train||msmarco-doctrain-qrels.tsv||7.6 MB||384,597||TREC qrels format|
|Train||msmarco-doctriples.py||-||-||Python script generates training triples|
|Dev||msmarco-docdev-queries.tsv||216 KB||5,193||tsv: qid, query|
|Dev||msmarco-docdev-top100||27 MB||519,300||TREC submission: qid, “Q0”, docid, rank, score, runstring|
|Dev||msmarco-docdev-qrels.tsv||112 KB||5,478||TREC qrels format|
|Test||msmarco-test2019-queries.tsv||12K||200||tsv: qid, query|
|Test||msmarco-doctest2019-top100||1.1M||20,000||TREC submission: qid, “Q0”, docid, rank, score, runstring|
|Test||2019qrels-docs||331K||16,258||qid, “Q0”, docid, rating|
This passage dataset is based on the public MS MARCO dataset, although our evaluation will be quite different. We will use a different set of test queries and we will use relevance judges to evaluate the quality of passage rankings in much more detail.
|Description||Filename||File size||Num Records||Format|
|Collection||collection.tar.gz||2.9 GB||8,841,823||tsv: pid, passage|
|Queries||queries.tar.gz||42.0 MB||1,010,916||tsv: qid, query|
|Qrels Dev||qrels.dev.tsv||1.1 MB||59,273||TREC qrels format|
|Qrels Train||qrels.train.tsv||10.1 MB||532,761||TREC qrels format|
|Queries, Passages, and Relevance Labels||collectionandqueries.tar.gz||2.9 GB||10,406,754|
|Train Triples Small||triples.train.small.tar.gz||27.1 GB||39,780,811||tsv: query, positive passage, negative passage|
|Train Triples Large||triples.train.full.tsv.gz||272.2 GB||397,756,691||tsv: query, positive passage, negative passage|
|Train Triples QID PID Format||qidpidtriples.train.full.2.tsv.gz||5.7 GB||397,768,673||tsv: qid, positive pid, negative pid|
|Top 1000 Train||top1000.train.tar.gz||175.0 GB||478,002,393||tsv: qid, pid, query, passage|
|Top 1000 Dev||top1000.dev.tar.gz||2.5 GB||6,668,967||tsv: qid, pid, query, passage|
|Test||msmarco-test2019-queries.tsv||12K||200||tsv: qid, query|
|Test||msmarco-passagetest2019-top1000.tsv||71M||189,877||tsv: qid, pid, query, passage|
|Test||2019qrels-pass.txt||182K||9,260||qid, “Q0”, docid, rating|
We will be following a similar format as the ones used by most TREC submissions, which is repeated below. White space is used to separate columns. The width of the columns in the format is not important, but it is important to have exactly six columns per line with at least one space between the columns.
1 Q0 pid1 1 2.73 runid1 1 Q0 pid2 1 2.71 runid1 1 Q0 pid3 1 2.61 runid1 1 Q0 pid4 1 2.05 runid1 1 Q0 pid5 1 1.89 runid1
As the official evaluation set, we provide a set of 200 queries (
msmarco-test2019-queries.tsv), where 50 or more will be judged by NIST assessors. For this purpose, NIST will be using depth pooling and construct separate pools for the passage ranking and document ranking tasks. Passages/documents in these pools will then be labelled by NIST assessors using multi-graded judgments, allowing us to measure NDCG. The same 200 queries are used for passage retrieval and document retrieval.
Besides our main evaluation using the NIST labels and NDCG, we also have sparse labels for the 200 queries, which already exist as part of the MS-Marco dataset. More information regarding how these sparse labels were obtained can be found at https://arxiv.org/abs/1611.09268. This allows us to calculate a secondary metric Mean Reciprocal Rank (MRR).
The main type of TREC submission is automatic, which means there was not manual intervention in running the test queries. This means you should not adjust your runs, rewrite the query, retrain your model, or make any other sorts of manual adjustments after you see the test queries. The ideal case is that you only look at the test queries to check that they ran properly (i.e. no bugs) then you submit your automatic runs. However, if you want to have a human in the loop for your run, or do anything else that uses the test queries to adjust your model or ranking, you can mark your run as manual. Manual runs are interesting, and we may learn a lot, but these are distinct from our main scenario which is a system that responds to unseen queries automatically.
Nick Craswell (Microsoft), Bhaskar Mitra (Microsoft), Emine Yilmaz (UCL) and Daniel Campos (Microsoft)
Since these are large files to download, here are the size in bytes and md5sum, as a reference.
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