task_categories:text-retrieval task_ids:document-retrieval multilinguality:monolingual size_categories:10M<n<100M language:en region:us
The Conversational Assistance Track (CAsT) is a new track for TREC 2019 to facilitate Conversational Information 
Seeking (CIS) research and to create a large-scale reusable test collection for conversational search systems. 
The document corpus is 38,426,252 passages from the TREC Complex Answer Retrieval (CAR) and Microsoft MAchine 
Reading COmprehension (MARCO) datasets.
@misc{dalton2020trec,
      title={TREC CAsT 2019: The Conversational Assistance Track Overview}, 
      author={Jeffrey Dalton and Chenyan Xiong and Jamie Callan},
      year={2020},
      eprint={2003.13624},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}
---
language:
- en
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
task_categories:
- text-retrieval
task_ids:
- document-retrieval
language_bcp47:
- en-US
---

# TREC Cast 2019 

[TREC Cast](http://www.treccast.ai) have released a document collection with topics and qrels of which a subset has been annotated such that it is suitable for multi-turn conversational search.

## Dataset statistics

- # Passages: 38,426,252
- # Topics: 20
- # Queries: 173

## Subsets

### CAR + MSMARCO Collection
Together CAR and MSMARCO have a size of 6,13G, so downloading will take a while. You can use the collection as followed:
```python
collection = load_dataset('trec-cast-2019-multi-turn', 'test_collection')
```

The collection has the following data format:
```
docno: str
  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}. E.g.: CAR_6869dee46ab12f0f7060874f7fc7b1c57d53144a
text: str
  The content of the passage.
```

#### Sample
Instead of using the entire data set, you can also download a sample set containing only 200,000 items:
```python
collection = load_dataset('trec-cast-2019-multi-turn', 'test_collection_sample')
```

### Topics
You can get the topics as followed:
```python
topics = load_dataset('trec-cast-2019-multi-turn', 'topics')
```

The topics have the following dataformat:
```
qid: str
  Query ID of the format "topicId_questionNumber"
history: str[]
  A list of queries. It can be empty for the first question in a topic.
query: str
  The query
```

### Qrels
You can get the qrels as followed:
```python
qrels = load_dataset('trec-cast-2019-multi-turn', 'qrels')
```

The qrels have the following data format:
```
qid: str
  Query ID of the format "topicId_questionNumber"
qrels: List[dict]
  A list of dictionaries with the keys 'docno' and 'relevance'. Relevance is an integer in the range [0, 4]
```