kiyam/ddro-nq-dataset

DDRO — NQ320K Processed Dataset

This dataset contains the preprocessed Natural Questions (NQ320K) corpus used to train and evaluate the DDRO generative retrieval models from:

📄 Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval (SIGIR 2025)

The raw NQ data (from Google) is processed into a unified format with document text, queries, and relevance annotations, ready for use in generative retrieval training pipelines.

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Files

File	Description	Size
nq_merged.json	Full NQ320K document corpus (109,739 unique docs, JSONL format)	5.5 GB
nq_train.gz	Training split — queries + document fields (TSV, gzipped)	6.5 GB
nq_val.gz	Validation split — queries + document fields (TSV, gzipped)	159 MB

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Format

nq_merged.json (JSONL)

One document per line:

{
  "id": "5655493461695504401",
  "query": "what is the most common use of opt-in email marketing",
  "long_answer": "...",
  "short_answer": "a newsletter sent to an advertising firm's customers",
  "title": "email marketing",
  "abstract": "...",
  "content": "...",
  "document_url": "https://en.wikipedia.org/...",
  "doc_tac": "<title> + <abstract> + <content>",
  "language": "en"
}

nq_train.gz / nq_val.gz (TSV, gzipped)

Tab-separated, no header. Columns: query, id, long_answer, short_answer, title, abstract, content, document_url, doc_tac, language

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Dataset Statistics

Split	Examples
Train	~307,373 (raw) → 87,791 (after dedup by title)
Dev	~7,830 (raw) → 21,948 (after dedup)
Unique documents	109,739

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Usage

Load the corpus

import json

docs = {}
with open("nq_merged.json") as f:
    for line in f:
        doc = json.loads(line)
        docs[doc["id"]] = doc

Load train/val splits

import gzip
import pandas as pd

columns = ["query", "id", "long_answer", "short_answer", "title",
           "abstract", "content", "document_url", "doc_tac", "language"]

with gzip.open("nq_train.gz", "rt") as f:
    train_df = pd.read_csv(f, sep="\t", names=columns)

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Corresponding Models

Trained on this dataset:

Model	Docid Type	MRR@10	R@10
kiyam/ddro-nq-pq	PQ (Product Quantization)	55.51	67.31
kiyam/ddro-nq-tu	TU (Title + URL)	45.99	55.98

SFT reference policies: kiyam/ddro-nq-pq-sft, kiyam/ddro-nq-tu-sft

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Source Data

This dataset is derived from the Google Natural Questions (NQ) dataset:

Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. Natural Questions: a Benchmark for Question Answering Research. Transactions of the Association of Computational Linguistics.

• Homepage: https://ai.google.com/research/NaturalQuestions
• License: Creative Commons Attribution-ShareAlike 3.0
• Raw files: gs://natural_questions/v1.0-simplified/

The original data is made available by Google under CC BY-SA 3.0. This processed version inherits the same license terms.

See process_nq_dataset.py for the full preprocessing pipeline.

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Citation

@inproceedings{amdie2025ddro,
  title={Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval},
  author={Amdie, Kidist},
  booktitle={Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2025},
}