# XOR QA: Cross-lingual Open-Retrieval Question Answering

arXiv: https://arxiv.org/abs/2010.11856 · HTML: https://mlsanity.com/paper/2010.11856/

## Key facts

- Cited by 121 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 30 of 121)

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- [EmbeddingGemma: Powerful and Lightweight Text Representations](https://mlsanity.com/paper/2509.20354/) (2025)
- [Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval](https://mlsanity.com/paper/2108.08787/) (2021)
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- [Gemini Embedding: Generalizable Embeddings from Gemini](https://mlsanity.com/paper/2503.07891/) (2025)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer](https://mlsanity.com/paper/2305.14857/) (2023)
- [Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinations](https://mlsanity.com/paper/2303.18027/) (2023)
- [HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attribution](https://mlsanity.com/paper/2307.16883/) (2023)
- [IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages](https://mlsanity.com/paper/2404.16816/) (2024)
- [Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation](https://mlsanity.com/paper/2206.10128/) (2022)
- [How Does Generative Retrieval Scale to Millions of Passages?](https://mlsanity.com/paper/2305.11841/) (2023)
- [Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval](https://mlsanity.com/paper/2203.05765/) (2022)
- [Questions Are All You Need to Train a Dense Passage Retriever](https://mlsanity.com/paper/2206.10658/) (2022)
- [Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages](https://mlsanity.com/paper/2210.09984/) (2022)
- [One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval](https://mlsanity.com/paper/2107.11976/) (2021)
- [AutoBencher: Towards Declarative Benchmark Construction](https://mlsanity.com/paper/2407.08351/) (2024)
- [Nonparametric Masked Language Modeling](https://mlsanity.com/paper/2212.01349/) (2022)
- [SD-QA: Spoken Dialectal Question Answering for the Real World](https://mlsanity.com/paper/2109.12072/) (2021)
- [XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages](https://mlsanity.com/paper/2305.11938/) (2023)
- [XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2210.13693/) (2022)
- [BERGEN: A Benchmarking Library for Retrieval-Augmented Generation](https://mlsanity.com/paper/2407.01102/) (2024)
- [ECLeKTic: a Novel Challenge Set for Evaluation of Cross-Lingual Knowledge Transfer](https://mlsanity.com/paper/2502.21228/) (2025)
- [Retrieval-augmented generation in multilingual settings](https://mlsanity.com/paper/2407.01463/) (2024)
- [Towards Best Practices for Training Multilingual Dense Retrieval Models](https://mlsanity.com/paper/2204.02363/) (2022)
- [English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings](https://mlsanity.com/paper/2211.06127/) (2022)
- [Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources](https://mlsanity.com/paper/2211.15649/) (2022)

## Abstract (arXiv)

Multilingual question answering tasks typically assume answers exist in the same language as the question. Yet in practice, many languages face both information scarcity -- where languages have few reference articles -- and information asymmetry -- where questions reference concepts from other cultures. This work extends open-retrieval question answering to a cross-lingual setting enabling questions from one language to be answered via answer content from another language. We construct a large-scale dataset built on questions from TyDi QA lacking same-language answers. Our task formulation, called Cross-lingual Open Retrieval Question Answering (XOR QA), includes 40k information-seeking questions from across 7 diverse non-English languages. Based on this dataset, we introduce three new tasks that involve cross-lingual document retrieval using multi-lingual and English resources. We establish baselines with state-of-the-art machine translation systems and cross-lingual pretrained models. Experimental results suggest that XOR QA is a challenging task that will facilitate the development of novel techniques for multilingual question answering. Our data and code are available at https://nlp.cs.washington.edu/xorqa.