| Challenge: | Open-domain question answering aims to answer questions through text retrieval and reading comprehension . but, the success of these models relies on a massive volume of training data, which is not available in other languages . a new dataset aims at investigating cross-lingual OpenQA . |
| Approach: | They propose to use a dataset for cross-lingual OpenQA research to test models . they use XQA dataset to train models with large volumes of labeled data . |
| Outcome: | The proposed model achieves best results in almost all target languages while the performance is lower than that of English. |
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| Challenge: | Existing work on product question answering systems focuses mainly on English, but in practice there is need to support multiple customer languages while leveraging product information available in English. |
| Approach: | They present a large-scale annotated cross-lingual PQA dataset in 12 languages and evaluate three approaches to generating a natural-sounding non-English answer. |
| Outcome: | The proposed dataset supports crosslingual product question answering (PQA) systems that provide answers to customers’ questions as they shop for products. |
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering (2021.tacl-1)
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| Challenge: | Existing multilingual QA datasets lack linguistic diversity and comparable evaluation between languages. |
| Approach: | They propose a multilingual question-answer evaluation set with 10k English queries and human translations of them into 25 additional languages and dialects. |
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Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation (2022.aacl-main)
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| Challenge: | Open-Domain Generative Question Answering has achieved impressive performance in English . combining document-level retrieval with answer generation can generate complete sentences . |
| Approach: | They propose an open-domain approach that combines document retrieval with answer generation to generate complete sentences in English . they propose a cross-lingual generative model that exploits passages written in multiple languages . |
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XOR QA: Cross-lingual Open-Retrieval Question Answering (2021.naacl-main)
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| Challenge: | a dataset of 40k information-seeking questions across seven languages is used to answer multilingual question answering tasks. |
| Approach: | They propose a task framework that allows questions from one language to be answered via answer content from another language. |
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CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training (2022.findings-naacl)
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| Challenge: | Existing approaches to answer open domain questions rely on unlabeled text or synthetically generated question-answer pairs. |
| Approach: | They propose a large-scale open-domain question-answering dataset based on the Common Crawl project that can be used to in-domain pre-train popular language models. |
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UQA: Corpus for Urdu Question Answering (2024.lrec-main)
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| Challenge: | Urdu is a low-resource language with over 70 million native speakers . expanding the reach of NLP to languages other than English is crucial for advancing multilingual AI systems. |
| Approach: | They introduce a novel dataset for question answering and text comprehension in Urdu . they use a technique called EATS which preserves the answer spans in translated context paragraphs . |
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Pre-training Cross-lingual Open Domain Question Answering with Large-scale Synthetic Supervision (2024.emnlp-main)
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| Challenge: | Cross-lingual open domain question answering requires multiple models, requiring substantial annotated datasets and auxiliary resources to bridge between languages. |
| Approach: | They propose a selfsupervised method that exploits Wikipedia's cross-lingual link structure . they show that the method outperforms comparable methods on supervised and zero-shot settings . |
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PolQA: Polish Question Answering Dataset (2024.lrec-main)
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| Challenge: | Recent proposed systems for open-domain question answering (OpenQA) require large amounts of training data to achieve state-of-the-art performance. |
| Approach: | They propose an efficient annotation strategy that increases passage retrieval accuracy@10 by 10.55 p.p. while reducing the annotation cost by 82%. |
| Outcome: | The proposed approach increases passage retrieval accuracy @10 by 10.55 p.p. while reducing the annotation cost by 82%. |
Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains (2023.eacl-demo)
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| Challenge: | a lack of cross-lingual training data in emergent domains makes it difficult to train on emerging domains. |
| Approach: | They propose a cross-lingual open-retrieval question answering system for COVID-19 . their system adopts a corpus of scientific articles to ensure that retrieved documents are reliable. |
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XLQA: A Benchmark for Locale-Aware Multilingual Open-Domain Question Answering (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown significant progress in Open-domain question answering (ODQA) but most evaluations focus on English and assume locale-invariant answers across languages. |
| Approach: | They propose a benchmark specifically designed for locale-sensitive multilingual ODQA that uses 3,000 English seed questions expanded to eight languages. |
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