---
language:
  - en
license: cc-by-sa-4.0
tags:
  - retrieval
  - text-retrieval
  - beir
  - stack-exchange
  - wordpress
  - community-question-answering
  - duplicate-questions
  - benchmark
pretty_name: BEIR CQADupStack WordPress (retrieval)
size_categories: "n<1K"
task_categories:
  - text-retrieval
---

# CQADupStack WordPress (BEIR) — duplicate-question retrieval

## Dataset description

**CQADupStack** is a benchmark for **community question answering (cQA)** built from publicly available **Stack Exchange** content. It was introduced by Hoogeveen, Verspoor, and Baldwin at **ADCS 2015** as a resource for studying **duplicate questions**: threads and posts are organized so that systems can be trained and evaluated on finding prior questions that match (or semantically duplicate) a newly asked question—central to reducing fragmentation and improving search on Q&A sites.

The original release aggregates material across **twelve** Stack Exchange forums. The **WordPress** subset corresponds to **[WordPress Stack Exchange](https://wordpress.stackexchange.com/)**—questions about WordPress development, themes, plugins, and site administration. Duplicate links come from the platform’s moderation workflow, with predefined splits so results stay comparable across papers.

**BEIR** (*Benchmarking IR*) repackaged CQADupStack—along with many other public corpora—as a standard **retrieval** benchmark for **zero-shot** evaluation of lexical, sparse, dense, and hybrid retrievers across heterogeneous tasks. In the BEIR formulation, **CQADupStack (WordPress)** is a **duplicate-question retrieval** setting: the “documents” are questions (or question-like posts) from the WordPress Stack Exchange corpus, and the task is to rank the true duplicate(s) for each query highly.

In upstream BEIR / **ir_datasets**, this slice is documented with on the order of **~49K** corpus documents, **541** test queries, and **744** qrels line items (binary relevance). Full retrieval evaluation requires indexing that **corpus** and ranking **queries** against it; this Hub repository exposes the **query + qrels** side in **Parquet** form for retrieval pipelines (aligned with the BEIR **test** split).

### Scale (this Hub snapshot)

| Split | Rows |
|-------|------|
| `test` | 541 |

Each row is one **query** with **relevance judgments** (`expected_output`) pointing at corpus document identifiers.

## Task: retrieval (CQADupStack WordPress)

The task is **ad hoc retrieval** specialized to **duplicate question finding** on the WordPress Stack Exchange domain:

1. **Input:** a natural-language **question** (the query)—typically about WordPress configuration, PHP hooks, themes, plugins, or the REST API.
2. **Output:** a ranked list of **document IDs** from the CQADupStack WordPress corpus (or scores over the full collection), such that **relevant** IDs—those marked as duplicates in the official qrels—appear at the top.

Standard IR metrics apply (e.g., **nDCG@k**, **Recall@k**, **MRR**), using the provided qrels as ground truth.

> **Note:** Align `expected_output` document IDs with the same **BEIR CQADupStack WordPress** corpus you use for indexing (same ID space as the upstream BEIR release).

## Data format (this repository)

Each record includes:

| Field | Description |
|--------|-------------|
| `id` | UUID for this example row. |
| `input` | The **query text** (Stack Exchange–style question). |
| `expected_output` | JSON string: list of objects `{"id": "<corpus-doc-id>", "score": <relevance>}`. Scores follow the BEIR qrels convention (typically `1` for relevant in binary settings). A query may have **one or more** relevant documents. |
| `metadata.query_id` | Original BEIR query identifier (string). |
| `metadata.split` | Split name; in this dataset, **`test`**. |

### Example 1 (single relevant document)

```json
{
  "id": "141baaae-cb5d-4cde-9987-91b40dcbf9cd",
  "input": "CPT admin column auto order by date instead of title",
  "expected_output": "[{\"id\": \"81939\", \"score\": 1}]",
  "metadata.query_id": "101834",
  "metadata.split": "test"
}
```

### Example 2 (multiple relevant documents)

```json
{
  "id": "a51c061a-64a8-4460-9c73-3db13e5d3cd0",
  "input": "Listing pages which uses specific template",
  "expected_output": "[{\"id\": \"29918\", \"score\": 1}, {\"id\": \"130919\", \"score\": 1}]",
  "metadata.query_id": "115020",
  "metadata.split": "test"
}
```

## References

### CQADupStack (original dataset)

**Doris Hoogeveen, Karin M. Verspoor, Timothy Baldwin**  
*CQADupStack: A Benchmark Data Set for Community Question-Answering Research*  
Proceedings of the 20th Australasian Document Computing Symposium (**ADCS 2015**), Parramatta, NSW, Australia.

- DOI: [10.1145/2838931.2838934](https://doi.org/10.1145/2838931.2838934)  
- PDF (author page): [ADCS 2015 paper](https://people.eng.unimelb.edu.au/tbaldwin/pubs/adcs2015.pdf)  
- Project page: [CQADupStack resources](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)  
- Code/data mirror: [CQADupStack on GitHub](https://github.com/D1Doris/CQADupStack)

The paper motivates duplicate-question tasks on real **Stack Exchange** communities and describes construction from a **Stack Exchange data dump**, including duplicate links and evaluation protocols suited to retrieval experiments.

### BEIR benchmark (CQADupStack as one of 18 datasets)

**Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, Iryna Gurevych**  
*BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models*  
NeurIPS 2021 (Datasets and Benchmarks Track).

**Abstract (from arXiv):** *“Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of-distribution (OOD) generalization capabilities. To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval. We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction and re-ranking architectures on the BEIR benchmark. Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs. In contrast, dense and sparse-retrieval models are computationally more efficient but often underperform other approaches, highlighting the considerable room for improvement in their generalization capabilities.”*

- Paper: [arXiv:2104.08663](https://arxiv.org/abs/2104.08663) — [OpenReview](https://openreview.net/forum?id=wCu6T5xFjeJ) (NeurIPS 2021 Datasets & Benchmarks)  
- Code and data: [BEIR on GitHub](https://github.com/beir-cellar/beir)

### Related resources

- **ir_datasets** documents BEIR slices with corpus/query/qrel counts: [beir/cqadupstack/wordpress](https://ir-datasets.com/beir.html) (search for `beir/cqadupstack/wordpress` on the page).  
- **MTEB** lists CQADupStack variants for embedding evaluation—useful for cross-checking task definitions: [MTEB on Hugging Face](https://huggingface.co/mteb).

## Citation

If you use **CQADupStack**, cite the ADCS 2015 paper above. If you use the **BEIR** packaging or evaluation protocol, cite the BEIR NeurIPS 2021 paper. If you use **this Parquet export**, cite both the original data sources and BEIR as appropriate for your experiment.

## License

Stack Exchange content is typically distributed under **Creative Commons** terms; BEIR and downstream cards commonly reference **`cc-by-sa-4.0`**. Verify against your corpus snapshot and upstream Stack Exchange / BEIR terms if you need strict compliance.

---

*Dataset card maintained for the `orgrctera/beir_cqadupstack_wordpress` Hub repository.*