There are currently few datasets appropriate for training and evaluating models for Conversational Information Seeking (CIS). The main aim of TREC CAsT is to advance research on conversational search systems. The goal of the track is to create a reusable benchmark for open-domain information centric conversational dialogues.
The track will run in 2020 and establish a concrete and standard collection of data with information needs to make systems directly comparable.
This is the second year of TREC CAsT, which will run as a track in TREC. This year we aim to focus on candidate information ranking in context:
- Read the dialogue context: Track the evolution of the information need in the conversation, identifying salient information needed for the current turn in the conversation
- Retrieve Candidate Response Information: Perform retrieval over a large collection of paragraphs (or knowledge base content) to identify relevant information
- NEW - Evaluation topics for Year 2 V1.0 - 25 primary evaluation topics in JSON and Protocol Buffer format. There are two variants automatic and manual.
- NEW - BM25 + BERT baseline - We provide a BM25 + BERT reranked baseline run for the raw utterances, automatically rewritten utterances, and the manually rewritten utterances.
- NEW - Interactive web UI - A simple web UI with the BM25 + BERT model used to create the baseline runs. No rewriting is performed.
- The corpus is a combination of two standard TREC collections: MARCO Ranking passages and Wikipedia (TREC CAR).
- The MS MARCO Passage Ranking collection - This file only includes the passage id and passage text. For convenience, we also provide a passage id -> URL mapping file in TSV format pid to URL file.
- The TREC CAR paragraph collection v2.0
- Year 2 task guidelines
- Note: Participants MUST REGISTER to submit (deadline end of May!).
- May 2020: Year 2 guidelines released
- July 2020: Year 2 evaluation topics released
- Twitter: @treccast
- Slack: treccast.slack.com
- Google groups [email protected]
- Training data release: See previous data
- Test topic release: July 9th
- Run submission: August 19th
- Jeff Dalton, University of Glasgow
- Chenyan Xiong, Microsoft Research
- Jamie Callan, Carnegie Mellon University
- Read the TREC 2019 Overview paper.
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Training topics year 1 V1.0 - 30 example training topics
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Training judgments - We provide limited (incomplete) training data for 5 topics (approximately 50 turns). These are judged from the baseline retrieval run (below). The judgments are graded on a three point scale (2 very relevant, 1 relevant, and 0 not relevant).
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Evaluation topics year 1 V1.0 - 50 evaluation topics
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Additional resources: MS MARCO Conversational Search Sessions Conversational Search data and train data is released.
- To facilitate work on passage ranking only we performed manual resolution of coreference as well as conversational ambiguity for topics. We make these available to participants who may not have access to automatic methods. Runs using this data manual runs. The annotations are provided in a tab separated format with the turn id (query id) and the rewritten query in text form.
- TRAIN: Sample annotations on two training queries (for exemplars)
- EVALUTION: Complete annotations on the evaluation topics for the year 1 evaluation queries.
- Indri search interface - We provide an Indri index of the CAsT collection. See the help page for details on indexing parameters and statistics. It includes a standard batch search API limited to 50 queries per batch.)
- Baseline retrieval - We provide the queries and run files in trec eval format: train queries, train run file, test queries, test run file - We provide an Indri baseline run with Query Likelihood run, including both the topics and run files. Queries are generated by running AllenNLP coreference resolution to perform rewriting and stopwords are removed using the Indri stopword list.
- The corpus is a combination of three standard TREC collections: MARCO Ranking passages, Wikipedia (TREC CAR), and News (Washington Post)
- The MS MARCO Passage Ranking collection - This file only includes the passage id and passage text. For convenience, we also provide a passage id -> URL mapping file in TSV format pid to URL file.
- The TREC CAR paragraph collection v2.0
- The TREC Washington Post Corpus version 2: Note this is behind a password and requires an organizational agreement, to obtain it see: https://ir.nist.gov/wapo/
- The document id format is
[collection_id_paragraph_id]with collection id and paragraph id separated by an underscore. - The collection ids are in the set:
{MARCO, CAR, WAPO}. - The paragraph ids are: standard provided by MARCO and CAR. For WAPO the paragraph ID is
[article_id-paragraph_index]where the paragraph_index is the starting from 1-based index of the paragraph using the provided paragraph markup separated by a single dash. - Example WaPo combined document id:
[WAPO_903cc1eab726b829294d1abdd755d5ab-1], or CAR:[CAR_6869dee46ab12f0f7060874f7fc7b1c57d53144a]
- Early analysis found that both the MARCO and WaPo corpora both contain a significant number of near duplicate paragraphs. We have run near-dupliate detection to cluster results; only one result per duplicate cluster will be evaluated. It is suggested that you remove dupliates (keeping the canonical document) from your indices.
- A README with the process and file format.
- Washington Post duplicate file
- MARCO duplicate file
- Note: The tools in the repository below require these files as input for processing the collection and perform deduplication when the data is generated.
- TREC-CAsT Tools repository with code and scripts for processing data.
- The tools contain scripts for parsing the collection into standard indexing formats. It also provides APIs for working with the topics (in text, json, and protocol buffer formats).
- Note: This will evolve over time, it currently contains topic definition files and scripts for reading and loading topics.