Papers with cross-lingual question answering
Evaluating and Modeling Attribution for Cross-Lingual Question Answering (2023.emnlp-main)
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Benjamin Muller, John Wieting, Jonathan Clark, Tom Kwiatkowski, Sebastian Ruder, Livio Soares, Roee Aharoni, Jonathan Herzig, Xinyi Wang
| Challenge: | Open-retrieval question answering systems are lacking in attribution for cross-lingual question answering . open-research questions are available in 20 languages, but their raw generation often falls short in factuality . |
| Approach: | They are the first to study attribution for cross-lingual question answering . they collect data in 5 languages to assess the attribution level of a state-of-the-art QA system . |
| Outcome: | The proposed approach improves the attribution level of a state-of-the-art cross-lingual QA system. |
Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension (2023.findings-eacl)
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| Challenge: | Existing high-quality xMRC datasets can be further utilized to fine-tune our model. |
| Approach: | They propose a cross-lingual question answering over knowledge base approach that converts KB subgraphs into passages to narrow the gap between KB schemas and questions. |
| Outcome: | The proposed approach outperforms baselines and achieves strong few-shot and zero-shot performance on two xKBQA datasets in 12 languages. |
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks (D19-1)
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| Challenge: | Existing models that can handle cross-lingual tasks with limited or no training data are insensitive to different languages. |
| Approach: | They propose to use Unicoder to train models in one language and apply it to other languages. |
| Outcome: | Experiments show that Unicoder learns the mappings among different languages from more perspectives. |
IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages (2024.acl-long)
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| Challenge: | IndicGenBench is the largest benchmark for evaluating large language models on user-facing generation tasks across a diverse set of 29 Indic languages . |
| Approach: | They evaluate large language models on user-facing generation tasks across 29 languages . they use human curation to provide multi-way parallel evaluation data for many under-represented languages a github repository . |
| Outcome: | IndicGenBench is the largest benchmark for evaluating LLMs on user-facing generation tasks across a diverse set of 29 Indic languages covering 13 scripts and 4 language families. |
Measuring User’s Mental Models of Speech Translation in Human-AI Collaboration (2026.acl-long)
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| Challenge: | Existing research on machine translation tools has not revealed how users perceive MT errors and how they evolve through interaction. |
| Approach: | They propose a framework where users accept MT output or request professional re-translation to answer questions based on information presented in a foreign language. |
| Outcome: | The proposed framework can predict where the system is likely to be wrong and how it evolves through interaction. |