| 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. |
| Outcome: | The proposed framework can be used to answer questions from one language to another . the dataset was built on 40K questions across 7 languages, but could not find same-language answers . |
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Odunayo Ogundepo, Tajuddeen Gwadabe, Clara Rivera, Jonathan Clark, Sebastian Ruder, David Adelani, Bonaventure Dossou, Abdou Diop, Claytone Sikasote, Gilles Hacheme, Happy Buzaaba, Ignatius Ezeani, Rooweither Mabuya, Salomey Osei, Chris Emezue, Albert Kahira, Shamsuddeen Muhammad, Akintunde Oladipo, Abraham Owodunni, Atnafu Tonja, Iyanuoluwa Shode, Akari Asai, Anuoluwapo Aremu, Ayodele Awokoya, Bernard Opoku, Chiamaka Chukwuneke, Christine Mwase, Clemencia Siro, Stephen Arthur, Tunde Ajayi, Verrah Otiende, Andre Rubungo, Boyd Sinkala, Daniel Ajisafe, Emeka Onwuegbuzia, Falalu Lawan, Ibrahim Ahmad, Jesujoba Alabi, Chinedu Mbonu, Mofetoluwa Adeyemi, Mofya Phiri, Orevaoghene Ahia, Ruqayya Iro, Sonia Adhiambo
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Towards more equitable question answering systems: How much more data do you need? (2021.acl-short)
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| Challenge: | Question answering datasets in English are relatively new, but lack of linguistic diversity in the field is a challenge. |
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| Challenge: | a lack of cross-lingual training data in emergent domains makes it difficult to train on emerging domains. |
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| 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 . |
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Multi-Domain Multilingual Question Answering (2021.emnlp-tutorials)
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| Challenge: | Question answering (QA) is one of the most challenging tasks in natural language processing. |
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TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages (2020.tacl-1)
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| Challenge: | Existing multilingual QA datasets lack linguistic diversity and comparable evaluation between languages. |
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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 . |
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xPQA: Cross-Lingual Product Question Answering in 12 Languages (2023.acl-industry)
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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. |
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McCrolin: Multi-consistency Cross-lingual Training for Retrieval Question Answering (2024.findings-emnlp)
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Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Lalita Lowphansirikul, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
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