Sunday 27 July 2025
Scientists have made a significant breakthrough in developing a system that can effectively retrieve fact-checked claims across languages. The innovative approach, known as QUST_ NLP, uses a three-stage framework to identify relevant information and has achieved impressive results.
The challenge lies in retrieving fact-checked claims from vast amounts of social media posts and news articles, written in different languages. The system must be able to understand the context, nuances, and subtle differences between languages to accurately retrieve relevant information. To overcome this hurdle, QUST_ NLP employs a mixed input strategy, combining both original text and machine-translated text.
The first stage of the framework involves retrieving candidate results from large datasets using pre-trained language models. These models are fine-tuned for each language to ensure optimal performance. The second stage involves re-ranking these candidates using specialized re-ranking models, which further refine the results based on their relevance to the input query.
The third and final stage is where QUST_ NLP truly shines. A weighted voting strategy is used to combine the outputs from multiple re-ranking models, ensuring that the most accurate and relevant information is retrieved. This ensemble approach allows the system to learn from its mistakes and adapt to new languages and contexts.
The results are nothing short of remarkable. In the monolingual track, QUST_ NLP achieved a success rate of 93.64%, ranking fifth among 28 participating teams. In the crosslingual track, it achieved a success rate of 79.25%, ranking seventh among 29 teams.
One of the key advantages of QUST_ NLP is its ability to handle languages with limited resources. By leveraging machine translation and fine-tuning models for each language, the system can effectively retrieve fact-checked claims even when there is limited data available.
The implications of this breakthrough are far-reaching. With QUST_ NLP, fact-checking organizations and researchers can now access a vast repository of verified information across languages, enabling them to make more informed decisions and debunk misinformation more efficiently.
In addition, the system has the potential to revolutionize language understanding and processing. By developing models that can learn from their mistakes and adapt to new contexts, QUST_ NLP paves the way for further advancements in natural language processing and machine translation.
As researchers continue to refine and expand the capabilities of QUST_ NLP, we can expect to see significant improvements in language understanding and information retrieval.
Cite this article: “Breakthrough in Fact-Checking: QUST_NLP System Retrieves Verified Information Across Languages”, The Science Archive, 2025.
Fact-Checking, Qust_Nlp, Natural Language Processing, Machine Translation, Language Models, Re-Ranking Models, Weighted Voting Strategy, Ensemble Approach, Crosslingual Track, Monolingual Track
