| Challenge: | Cross-lingual question answering systems are becoming more and more important . a new approach can be generalized to more than 20 languages and outperforms previous models by 12% . |
| Approach: | They propose a cross-lingual question answering system that can be generalized to more than 20 languages . their approach can outperform previous models by 12% on multiple languages based on a dataset . |
| Outcome: | The proposed approach outperforms the previous models on multiple languages by 12% . it can be generalized to more than 20 languages and outperformed all previous models by 2% . |
Similar Papers
XOR QA: Cross-lingual Open-Retrieval Question Answering (2021.naacl-main)
Copied to clipboard
| 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 . |
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to improve Question Answering performance on non-English data are expensive and limited to evaluation set. |
| Approach: | They propose a method to improve Question Answering performance without additional annotations by leveraging Question Generation models to produce synthetic samples in a cross-lingual fashion. |
| Outcome: | The proposed method outperforms baselines on four datasets in English significantly . the proposed model outperformed baselines in english and is comparable to the validation set of the original SQuAD. |
Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation (2022.aacl-main)
Copied to clipboard
| 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 . |
| Outcome: | The proposed model outperforms answer sentence selection baselines for all 5 languages and monolingual pipelines for three out of five languages. |
Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains (2023.eacl-demo)
Copied to clipboard
| 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. |
| Outcome: | The proposed system outperforms BM25 baselines in cross-lingual settings. |
Cross-lingual Open-Retrieval Question Answering for African Languages (2023.findings-emnlp)
Copied to clipboard
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
| Challenge: | Our Dataset is the first cross-lingual QA dataset with a focus on African languages. |
| Approach: | They propose to use African languages as the only high-coverage source of answer content for cross-lingual open-retrieval question answering systems. |
| Outcome: | Our Dataset includes 12,000+ XOR QA examples across 10 African languages. |
Cross-Lingual Phrase Retrieval (2022.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to cross-lingual phrase retrieval learn word or sentence representations in word or sentences. |
| Approach: | They propose a cross-lingual phrase retrieval model that extracts phrase representations from unlabeled example sentences. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a large-scale cross-lingual phrase retrieval dataset, showing it can perform in an unseen language pair during training. |
Towards more equitable question answering systems: How much more data do you need? (2021.acl-short)
Copied to clipboard
| Challenge: | Question answering datasets in English are relatively new, but lack of linguistic diversity in the field is a challenge. |
| Approach: | They propose to use translation and cross-lingual transfer to produce QA systems in multiple languages to improve their performance. |
| Outcome: | The proposed approaches take advantage of existing resources to produce QA systems in multiple languages. |
Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Question Answering Task (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing studies focus on English as the data language for RAG, resulting in limited coverage of multilingual RAG. |
| Approach: | They propose a method that translates retrieved documents into a common language before generating the response. |
| Outcome: | The proposed approach improves efficiency on knowledge-intensive tasks but introduces inconsistencies due to cross-lingual variations in the retrieved content. |
Pre-training Cross-lingual Open Domain Question Answering with Large-scale Synthetic Supervision (2024.emnlp-main)
Copied to clipboard
| 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 . |
| Outcome: | The proposed method outperforms comparable methods on supervised and zero-shot language adaptation settings. |
XQA: A Cross-lingual Open-domain Question Answering Dataset (P19-1)
Copied to clipboard
| 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. |