README.md

---
language:
- en
task_categories:
- text-retrieval
size_categories:
- 10K<n<100K
dataset_info:
- config_name: documents
  features:
  - name: chunk_id
    dtype: string
  - name: chunk
    dtype: string
  - name: source_url
    dtype: string
  - name: title
    dtype: string
  - name: chunk_idx
    dtype: int64
  - name: chunk_start_char
    dtype: int64
  - name: chunk_end_char
    dtype: int64
  splits:
  - name: test
    num_bytes: 9874260
    num_examples: 16063
  download_size: 5348207
  dataset_size: 9874260
- config_name: documents_coref
  features:
  - name: chunk_id
    dtype: string
  - name: chunk
    dtype: string
  - name: source_url
    dtype: string
  - name: title
    dtype: string
  - name: chunk_idx
    dtype: int64
  - name: chunk_start_char
    dtype: int64
  - name: chunk_end_char
    dtype: int64
  splits:
  - name: test
    num_bytes: 10969007
    num_examples: 16063
  download_size: 5561426
  dataset_size: 10969007
- config_name: queries
  features:
  - name: original_query
    dtype: string
  - name: query
    dtype: string
  - name: answer
    list: string
  - name: score
    list: int64
  - name: source_url
    list: string
  - name: frag_start_char
    list: int64
  - name: frag_end_char
    list: int64
  - name: n_gold
    dtype: int64
  - name: categories
    list: string
  - name: url
    dtype: string
  splits:
  - name: test
    num_bytes: 436564
    num_examples: 479
  download_size: 264060
  dataset_size: 436564
configs:
- config_name: documents
  data_files:
  - split: test
    path: documents/test-*
- config_name: documents_coref
  data_files:
  - split: test
    path: documents_coref/test-*
- config_name: queries
  data_files:
  - split: test
    path: queries/test-*
---

# DAPR NQ-Hard — Chunk-level Retrieval Eval

Contextualized chunk-level (query2chunk) retrieval eval for **NQ-Hard**, the hard subset of
Natural Questions from the [DAPR benchmark](https://huggingface.co/datasets/UKPLab/dapr)
(*Document-Aware Passage Retrieval*, Wang, Reimers & Gurevych, ACL 2024,
[arXiv:2305.13915](https://arxiv.org/abs/2305.13915)).

NQ-Hard queries are hand-selected because **understanding the document context is required**
to retrieve the relevant passage — the gold passage often refers to the query's entity only
by coreference, main-topic ellipsis, an acronym, or via multi-hop reasoning. This makes it a
focused probe of context-aware retrieval.

## Corpus scope: gold-document-scoped

Each hard query is *about* one Wikipedia document (`query_id == doc_id`). The corpus here is
the union of **all passages of the 479 query documents** — i.e. each query must find its gold
passage(s) among the passages of the relevant document set.

> This is the **gold-document-scoped** setting: a lightweight, self-contained probe of
> context-dependent passage selection (~16k passages, encodes in seconds). It is **easier**
> than DAPR's canonical full-corpus NQ setting (retrieval over 2.68M passages) and scores are
> **not directly comparable** to published DAPR numbers.

| | count |
| --- | --- |
| queries | 479 |
| gold (query, passage) pairs | 516 |
| corpus passages | 16,063 |
| documents | 479 (mean 33.5 passages/doc, max 228) |

### Hardness categories (per gold pair)
| category | pairs |
| --- | --- |
| coreference | 223 |
| main_topic | 205 |
| multi-hop | 88 |
| acronym | 13 |

A pair may have multiple categories. Use the `queries.categories` field to slice metrics by
reasoning type — e.g. measure whether the `documents_coref` variant specifically lifts the
223 **coreference** queries.

## Configs

### `documents` / `documents_coref`
| field | type | notes |
| --- | --- | --- |
| `chunk_id` | string | DAPR passage id (`{doc}-{paragraph}`) |
| `chunk` | string | passage text (coreference-resolved in `documents_coref`) |
| `source_url` | string | document id |
| `title` | string | document title |
| `chunk_idx` | int64 | paragraph number within the document |
| `chunk_start_char` / `chunk_end_char` | int64 | char offset of the passage in the reconstructed document |

> `documents_coref` reuses the same char offsets as `documents` (plain-text coordinate
> space) so the single `queries` config drives overlap-based gold matching for both configs;
> only the `chunk` text differs.

### `queries`
| field | type | notes |
| --- | --- | --- |
| `original_query` / `query` | string | the query text |
| `answer` | list[string] | gold passage text(s) |
| `score` | list[int64] | relevance per gold passage |
| `source_url` | list[string] | document id per gold passage |
| `frag_start_char` / `frag_end_char` | list[int64] | char span of each gold passage |
| `n_gold` | int64 | number of gold passages |
| `categories` | list[string] | hardness reason(s): coreference / main_topic / multi-hop / acronym |
| `url` | string | source Wikipedia URL |

## Gold matching

A chunk is gold if it shares `source_url` with a gold fragment and overlaps its
`[frag_start_char, frag_end_char)`. Each gold fragment is a whole passage and each chunk is a
whole passage, so every gold maps to **exactly one** chunk (verified: 516 / 516 in both
`documents` and `documents_coref`).

## Provenance

Built from `UKPLab/dapr` `nq-hard` (queries + gold, with categories/url) and the DAPR
`NaturalQuestions-corpus` / `-corpus_coref` (test), filtered to the 479 gold documents.
Documents are reconstructed by concatenating passages in `paragraph_no` order (joined with
`\n`) to assign char offsets.

## Citation

```bibtex
@article{wang2023dapr,
    title  = "DAPR: A Benchmark on Document-Aware Passage Retrieval",
    author = "Kexin Wang and Nils Reimers and Iryna Gurevych",
    journal= "arXiv preprint arXiv:2305.13915",
    year   = "2023",
    url    = "https://arxiv.org/abs/2305.13915"
}
```