Before revision:  To prove this argument, we propose a simple but effective zero-shot framework, CogCompXEL, that complements current methods by utilizing query log mapping files from online search engines . CogCompXEL outperforms current state-of-the-art models on almost all 25 languages of the LORELEI dataset, achieving an absolute average increase of 25 \% in gold candidate recall.
After revision:  To prove this argument, we propose a simple but effective zero-shot framework, CogCompXEL, that complements current methods by utilizing query log mapping files from online search engines . CogCompXEL outperforms current state-of-the-art models on almost all 25 languages , QuEL~shows an average increase of 25 \% in gold candidate recall.