TREC 2022 Deep Learning Track Guidelines

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December 2021 note: Detailed guidelines will be available in 2022. We expect to keep using the v2 data. We may continue to stratify by query length. We may set up judging that focuses on highly-relevant results. Since the training and dev set results were somewhat less predictive of final NDCG@10, we may suggest methods for handling the sparse labels that bring things more in line with NDCG.

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).

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. The Deep Learning Track organized in 2019 and 2020 aimed at providing large scale datasets to TREC, and create a focused research effort with a rigorous blind evaluation of ranker for the passage ranking and document ranking tasks.

In 2022, the track will continue to have the same tasks (document ranking and passage ranking) and goals. Similar to the previous year, one of the main goals of the track in 2022 is to study what methods work best when a large amount of training data is available. 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 models can be brought in to bear in this scenario, and how useful is it to combine full supervision with other forms of supervision?

Deep Learning Track Tasks

The Deep Learning Track has two tasks: Passage ranking and document ranking; and two subtasks in each case: full ranking and reranking. You can submit up to three runs for each of the subtasks.

Each task uses 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 infer document-level labels from the passage-level labels.

For both tasks, the participants are encouraged to study the efficacy of transfer learning methods. Our current training labels (from MS MARCO) are generated differently than the test labels (generated by NIST), although some labels from past years (mapped to the new corpus) may also be available. Participants can (and are encouraged to) also use external corpora for large scale language model pretraining, or adapt algorithms built for one task of the track (e.g. passage ranking) to the other task (e.g. document ranking). This allows participants to study a variety of transfer learning strategies.

Below the two tasks are described in more detail.

Document Ranking Task

The first task focuses on document ranking. We have two subtasks related to this: Full ranking and top-100 reranking.

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 100 documents for this task. It models a scenario where you are building an end-to-end retrieval system.

In the reranking subtask, we provide you with an initial ranking of 100 documents from a simple IR system, and you are expected to rerank 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 reranking. The reranking subtask allows participants to focus on reranking only, without needing to implement an end-to-end system. It also makes those reranking runs more comparable, because they all start from the same set of 100 candidates.

Passage Ranking Task

Similar to the document ranking task, the passage ranking task also has a full ranking and reranking 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 100 passages for this end-to-end retrieval task.

In context of top-100 reranking subtask, we provide you with an initial ranking of 100 passages and you are expected to rerank these passages based on their likelihood of containing an answer to the question. In this subtask, we can compare different reranking methods based on the same initial set of 100 candidates, with the same rationale as described for the document reranking subtask.



Submission, evaluation and judging

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    2 2.71 runid1
1 Q0 pid3    3 2.61 runid1
1 Q0 pid4    4 2.05 runid1
1 Q0 pid5    5 1.89 runid1

, where:

As the official evaluation set, we provide a set of test queries, where a subset 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 test 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 test queries, which already exist as part of the MS-Marco dataset. More information regarding how these sparse labels were obtained can be found at This allows us to calculate a secondary metric Mean Reciprocal Rank (MRR). For the full ranking setting, we also compute NCG to evaluate the performance of the candidate generation stage.

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.


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