mlsanityData to 14 Oct 2026

Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation

arXiv 2110.07150 ↗First posted 14 Oct 2021cs.CLcited by 6

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Abstract (arXiv)

Open-Domain Generative Question Answering has achieved impressive performance in English by combining document-level retrieval with answer generation. These approaches, which we refer to as GenQA, can generate complete sentences, effectively answering both factoid and non-factoid questions. In this paper, we extend GenQA to the multilingual and cross-lingual settings. For this purpose, we first introduce GenTyDiQA, an extension of the TyDiQA dataset with well-formed and complete answers for Arabic, Bengali, English, Japanese, and Russian. Based on GenTyDiQA, we design a cross-lingual generative model that produces full-sentence answers by exploiting passages written in multiple languages, including languages different from the question. Our cross-lingual generative system outperforms answer sentence selection baselines for all 5 languages and monolingual generative pipelines for three out of five languages studied.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 42 of its 74 references that are in the map. First posted on arXiv 14 Oct 2021.

Builds on

42 of 74 references.

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30 more
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Cited by year

Citing papers per year: 2023: 3; 2024: 2; 2025: 1.

6 papers.

  1. 2024Retrieval-augmented generation in multilingual settingscited by 27
  2. 2023Evaluating and Modeling Attribution for Cross-Lingual Question Answeringcited by 21
  3. 2025Identifying and Investigating Global News Coverage of Critical Events Such as Disasters and Terrorist Attackscited by 4
  4. 2023Learning Answer Generation using Supervision from Automatic Question Answering Evaluatorscited by 3
  5. 2024Towards Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in the Era of Large Language Modelscited by 1
  6. 2023Structural Self-Supervised Objectives for Transformerscited by 0