%0 Conference Proceedings %T Cognition-aware Cognate Detection %A Kanojia, Diptesh %A Sharma, Prashant %A Ghodekar, Sayali %A Bhattacharyya, Pushpak %A Haffari, Gholamreza %A Kulkarni, Malhar %Y Merlo, Paola %Y Tiedemann, Jorg %Y Tsarfaty, Reut %S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume %D 2021 %8 April %I Association for Computational Linguistics %C Online %F kanojia-etal-2021-cognition %X Automatic detection of cognates helps downstream NLP tasks of Machine Translation, Cross-lingual Information Retrieval, Computational Phylogenetics and Cross-lingual Named Entity Recognition. Previous approaches for the task of cognate detection use orthographic, phonetic and semantic similarity based features sets. In this paper, we propose a novel method for enriching the feature sets, with cognitive features extracted from human readers’ gaze behaviour. We collect gaze behaviour data for a small sample of cognates and show that extracted cognitive features help the task of cognate detection. However, gaze data collection and annotation is a costly task. We use the collected gaze behaviour data to predict cognitive features for a larger sample and show that predicted cognitive features, also, significantly improve the task performance. We report improvements of 10% with the collected gaze features, and 12% using the predicted gaze features, over the previously proposed approaches. Furthermore, we release the collected gaze behaviour data along with our code and cross-lingual models. %R 10.18653/v1/2021.eacl-main.288 %U https://aclanthology.org/2021.eacl-main.288 %U https://doi.org/10.18653/v1/2021.eacl-main.288 %P 3281-3292