---
language:
  - en
license: cc-by-sa-4.0
tags:
  - retrieval
  - text-retrieval
  - beir
  - duplicate-question-detection
  - stack-exchange
  - gaming
  - benchmark
pretty_name: BEIR CQADupStack — Gaming (retrieval)
size_categories: "1K<n<10K"
task_categories:
  - text-retrieval
---

# CQADupStack — Gaming (BEIR)

## Dataset description

**CQADupStack** is a benchmark for **community question answering (cQA)** research built from **Stack Exchange** forums. It was introduced by Hoogeveen, Verspoor, and Baldwin at **ADCS 2015**. The collection spans **twelve** topical subcommunities (tags); each subforum is distributed with **duplicate-question annotations** derived from how users and moderators linked related threads. That makes CQADupStack a standard resource for studying **duplicate question retrieval** and related tasks over noisy, real-world Q&A text.

**BEIR** (*Benchmarking IR*, Thakur et al., NeurIPS 2021) repackages CQADupStack—along with many other public corpora—as a unified **zero-shot information retrieval** benchmark. BEIR treats each CQADupStack **subforum** (including **Gaming**) as a separate retrieval setting: a **query** is a question post, and **relevant documents** are other question posts marked as duplicates (or in the duplicate closure), evaluated against a **corpus** of posts from the same domain.

This repository (`orgrctera/beir_cqadupstack_gaming`) holds the **Gaming** slice in **Parquet** form for retrieval pipelines. Rows correspond to **evaluation queries** with **relevance judgments** (`expected_output`) pointing at **corpus document identifiers** from the BEIR **cqadupstack-gaming** corpus. The released split is **`test`** (1,595 examples), consistent with BEIR’s standard test evaluation for this subset.

### Domain

The **Gaming** forum covers video games, platforms (e.g., PC/console), mechanics, compatibility, and performance—typical Stack Exchange *Arqade*–style content. Language is **English**.

## Task: retrieval (CQADupStack Gaming)

The task is **duplicate-question retrieval** formulated as **ad hoc retrieval**:

1. **Input:** a natural-language **question** (the query).
2. **Output:** a ranked list of **document IDs** from the **Gaming** corpus (or scores over the full collection), such that documents judged **relevant** in the official qrels receive high rank.

Evaluation uses standard IR metrics (e.g., **nDCG@k**, **Recall@k**, **MRR**), as in BEIR’s evaluation scripts or compatible toolkits (Pyserini, ir-measures, etc.).

> **Note:** Full retrieval evaluation requires the **corpus** (passages keyed by ID). Align `expected_output` document IDs with the same BEIR **cqadupstack-gaming** corpus you index for search.

## Data format (this repository)

| Field | Description |
|--------|-------------|
| `id` | UUID for this example row. |
| `input` | The **query text** (question). |
| `expected_output` | JSON string: list of objects `{"id": "<corpus-doc-id>", "score": <relevance>}`. Scores follow BEIR qrels (typically `1` for relevant in binary settings). |
| `metadata.query_id` | Original BEIR / CQADupStack Gaming query identifier (string). |
| `metadata.split` | Split name; this dataset provides **`test`**. |

### Example 1

```json
{
  "id": "bd7ad6eb-d9af-4f9e-b48b-2458e9e61afd",
  "input": "Are the XBLA and PC versions of Minecraft compatible?",
  "expected_output": "[{\"id\": \"143450\", \"score\": 1}]",
  "metadata.query_id": "65828",
  "metadata.split": "test"
}
```

### Example 2

```json
{
  "id": "99cb0b75-167c-47d9-a103-381d8e4930ec",
  "input": "How to speed up minecraft on my computer",
  "expected_output": "[{\"id\": \"14876\", \"score\": 1}, {\"id\": \"24836\", \"score\": 1}]",
  "metadata.query_id": "42307",
  "metadata.split": "test"
}
```

## References

### Original CQADupStack resource

**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**), pages 3:1–3:8.

- DOI: [10.1145/2838931.2838934](https://doi.org/10.1145/2838931.2838934)  
- Original data and documentation: [University of Melbourne NLP resources — CQADupStack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)

The paper presents the full multi-forum benchmark, duplicate annotations, and evaluation protocols for community QA research.

### BEIR benchmark (CQADupStack as a retrieval task)

**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)  
- Code and dataset index: [BEIR on GitHub](https://github.com/beir-cellar/beir)

### Related Hugging Face resources

- Raw BEIR-style layouts (corpus / queries / qrels) appear in community mirrors such as [`BeIR/cqadupstack-*`](https://huggingface.co/datasets?search=beir+cqadupstack) datasets.  
- **ir-datasets** packaging: [ir-datasets BEIR documentation](https://ir-datasets.com/beir) for programmatic access to BEIR collections.

## Citation

If you use **CQADupStack**, cite Hoogeveen et al. (ADCS 2015). If you use the **BEIR** packaging or evaluation protocol, also cite Thakur et al. (NeurIPS 2021). BibTeX for BEIR is available from the [official repository](https://github.com/beir-cellar/beir).

## License

Stack Exchange content is typically shared under **Creative Commons** terms (e.g., **CC BY-SA**). This card uses **`cc-by-sa-4.0`** as a common designation for Stack Exchange–derived text; confirm against your corpus snapshot and [Stack Exchange’s current licensing terms](https://stackoverflow.com/help/licensing) if strict compliance is required.

---

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